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1b4d259 | 1 | {"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.12.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"gpu","dataSources":[{"sourceId":14628798,"sourceType":"datasetVersion","datasetId":1429416}],"dockerImageVersionId":31260,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true},"colab":{"provenance":[],"gpuType":"T4"},"accelerator":"GPU"},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"id":"yhVNR6GETKyA"},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"#### **ps2p means revised extract_camera_params_process2 and includes color infomation**","metadata":{}},{"cell_type":"code","source":"# =====================================================================\n# biplet_dino_mast3r_ps2_gs_colab_01.ipynb\n# ASMK を DINO に置き換えたバージョン\n# =====================================================================\n\n# =====================================================================\n# CELL 1: Install Dependencies\n# =====================================================================\n!pip install roma einops timm huggingface_hub\n!pip install opencv-python pillow tqdm pyaml cython plyfile\n!pip install pycolmap trimesh\n!pip install transformers==4.40.0 # DINOに必要\n!pip uninstall -y numpy scipy\n!pip install numpy==1.26.4 scipy==1.11.4\nbreak","metadata":{"trusted":true,"id":"6C3QGJD8TKyC","outputId":"b362f97d-fbc1-474f-f2cb-b84b565acdb9"},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"id":"TPcj5qcmedBw","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# restart & run after\n# =====================================================================\n# CELL 2: Mount Drive and Verify\n# =====================================================================\n\nimport numpy as np\nprint(f\"✓ np: {np.__version__} - {np.__file__}\")\n!pip show numpy | grep Version\n\ntry:\n import roma\n print(\"✓ roma is installed\")\nexcept ModuleNotFoundError:\n print(\"⚠️ roma not found, installing...\")\n !pip install roma\n import roma\n print(\"✓ roma installed\")\n\n# =====================================================================\n# CELL 3: Clone Repositories\n# =====================================================================\nimport os\nimport sys\n\n# MASt3Rをクローン\nif not os.path.exists('/kaggle/working/mast3r'):\n print(\"Cloning MASt3R repository...\")\n !git clone --recursive https://github.com/naver/mast3r.git /kaggle/working/mast3r\n print(\"✓ MASt3R cloned\")\nelse:\n print(\"✓ MASt3R already exists\")\n\n# DUSt3Rをクローン（MASt3R内に必要）\nif not os.path.exists('/kaggle/working/mast3r/dust3r'):\n print(\"Cloning DUSt3R repository...\")\n !git clone --recursive https://github.com/naver/dust3r.git /kaggle/working/mast3r/dust3r\n print(\"✓ DUSt3R cloned\")\nelse:\n print(\"✓ DUSt3R already exists\")\n\n# パスを追加\nsys.path.insert(0, '/kaggle/working/mast3r')\nsys.path.insert(0, '/kaggle/working/mast3r/dust3r')\n\n# 確認\ntry:\n from dust3r.model import AsymmetricCroCo3DStereo\n print(\"✓ dust3r.model imported successfully\")\nexcept ImportError as e:\n print(f\"✗ Import error: {e}\")\n\n# croco（MASt3Rの依存関係）もクローン\nif not os.path.exists('/kaggle/working/mast3r/croco'):\n print(\"Cloning CroCo repository...\")\n !git clone --recursive https://github.com/naver/croco.git /kaggle/working/mast3r/croco\n print(\"✓ CroCo cloned\")\n\n# =====================================================================\n# CELL 4: Clone and Build Gaussian Splatting\n# =====================================================================\nprint(\"\\n\" + \"=\"*70)\nprint(\"STEP: Clone Gaussian Splatting\")\nprint(\"=\"*70)\nWORK_DIR = \"/kaggle/working/gaussian-splatting\"\n\nimport subprocess\nif not os.path.exists(WORK_DIR):\n subprocess.run([\n \"git\", \"clone\", \"--recursive\",\n \"https://github.com/graphdeco-inria/gaussian-splatting.git\",\n WORK_DIR\n ], capture_output=True)\n print(\"✓ Cloned\")\nelse:\n print(\"✓ Already exists\")\n\n# インストールが必要なディレクトリ\nsubmodules = [\n \"/kaggle/working/gaussian-splatting/submodules/diff-gaussian-rasterization\",\n \"/kaggle/working/gaussian-splatting/submodules/simple-knn\"\n]\n\nfor path in submodules:\n print(f\"Installing {path}...\")\n subprocess.run([\"pip\", \"install\", path], check=True)\n\nprint(\"✓ Custom CUDA modules installed.\")\n\nprint(f\"✓ np: {np.__version__} - {np.__file__}\")\n!pip show numpy | grep Version\n\n# =====================================================================\n# CELL 5: Import Core Libraries and Configure Memory\n# =====================================================================\nimport os\nimport sys\nimport gc\nimport torch\nimport numpy as np\nfrom pathlib import Path\nfrom tqdm import tqdm\nimport torch.nn.functional as F\nimport shutil\nfrom PIL import Image\nfrom transformers import AutoImageProcessor, AutoModel\n\n# MEMORY MANAGEMENT\nos.environ['PYTORCH_CUDA_ALLOC_CONF'] = 'expandable_segments:True'\n\ndef clear_memory():\n \"\"\"メモリクリア関数\"\"\"\n gc.collect()\n if torch.cuda.is_available():\n torch.cuda.empty_cache()\n torch.cuda.synchronize()\n\ndef get_memory_info():\n \"\"\"Get current memory usage\"\"\"\n if torch.cuda.is_available():\n allocated = torch.cuda.memory_allocated() / 1024**3\n reserved = torch.cuda.memory_reserved() / 1024**3\n print(f\"GPU Memory - Allocated: {allocated:.2f}GB, Reserved: {reserved:.2f}GB\")\n\n import psutil\n cpu_mem = psutil.virtual_memory().percent\n print(f\"CPU Memory Usage: {cpu_mem:.1f}%\")\n\n# CONFIGURATION\nclass Config:\n DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n MAST3R_WEIGHTS = \"naver/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric\"\n DUST3R_WEIGHTS = \"naver/DUSt3R_ViTLarge_BaseDecoder_512_dpt\"\n\n # DINO設定\n DINO_MODEL = \"facebook/dinov2-base\"\n GLOBAL_TOPK = 20 # 各画像がペアを組む上位K個\n\n IMAGE_SIZE = 224\n\n# =====================================================================\n# CELL 6: Image Preprocessing Functions (Biplet)\n# =====================================================================\ndef normalize_image_sizes_biplet(input_dir, output_dir=None, size=1024):\n \"\"\"\n Generates two square crops (Left & Right or Top & Bottom)\n from each image in a directory.\n \"\"\"\n if output_dir is None:\n output_dir = input_dir + \"_biplet\"\n\n os.makedirs(output_dir, exist_ok=True)\n\n print(f\"\\n=== Generating Biplet Crops ({size}x{size}) ===\")\n\n converted_count = 0\n size_stats = {}\n\n for img_file in tqdm(sorted(os.listdir(input_dir)), desc=\"Creating biplets\"):\n if not img_file.lower().endswith(('.jpg', '.jpeg', '.png')):\n continue\n\n input_path = os.path.join(input_dir, img_file)\n\n try:\n img = Image.open(input_path)\n original_size = img.size\n\n size_key = f\"{original_size[0]}x{original_size[1]}\"\n size_stats[size_key] = size_stats.get(size_key, 0) + 1\n\n # Generate 2 crops\n crops = generate_two_crops(img, size)\n\n base_name, ext = os.path.splitext(img_file)\n for mode, cropped_img in crops.items():\n output_path = os.path.join(output_dir, f\"{base_name}_{mode}{ext}\")\n cropped_img.save(output_path, quality=95)\n\n converted_count += 1\n\n except Exception as e:\n print(f\" ✗ Error processing {img_file}: {e}\")\n\n print(f\"\\n✓ Biplet generation complete:\")\n print(f\" Source images: {converted_count}\")\n print(f\" Biplet crops generated: {converted_count * 2}\")\n print(f\" Original size distribution: {size_stats}\")\n\n return output_dir\n\n\ndef generate_two_crops(img, size):\n \"\"\"\n Crops the image into a square and returns 2 variations\n \"\"\"\n width, height = img.size\n crop_size = min(width, height)\n crops = {}\n\n if width > height:\n # Landscape → Left & Right\n positions = {\n 'left': 0,\n 'right': width - crop_size\n }\n for mode, x_offset in positions.items():\n box = (x_offset, 0, x_offset + crop_size, crop_size)\n crops[mode] = img.crop(box).resize(\n (size, size),\n Image.Resampling.LANCZOS\n )\n else:\n # Portrait or Square → Top & Bottom\n positions = {\n 'top': 0,\n 'bottom': height - crop_size\n }\n for mode, y_offset in positions.items():\n box = (0, y_offset, crop_size, y_offset + crop_size)\n crops[mode] = img.crop(box).resize(\n (size, size),\n Image.Resampling.LANCZOS\n )\n\n return crops\n\n# =====================================================================\n# CELL 7: Image Loading Function\n# =====================================================================\ndef load_images_from_directory(image_dir, max_images=200):\n \"\"\"ディレクトリから画像をロード\"\"\"\n print(f\"\\nLoading images from: {image_dir}\")\n\n valid_extensions = {'.jpg', '.jpeg', '.png', '.bmp'}\n image_paths = []\n\n for ext in valid_extensions:\n image_paths.extend(sorted(Path(image_dir).glob(f'*{ext}')))\n image_paths.extend(sorted(Path(image_dir).glob(f'*{ext.upper()}')))\n\n image_paths = sorted(set(str(p) for p in image_paths))\n\n if len(image_paths) > max_images:\n print(f\"⚠️ Limiting from {len(image_paths)} to {max_images} images\")\n image_paths = image_paths[:max_images]\n\n print(f\"✓ Found {len(image_paths)} images\")\n return image_paths\n\n# =====================================================================\n# CELL 8: MASt3R Model Loading\n# =====================================================================\ndef load_mast3r_model(device):\n \"\"\"MASt3Rモデルをロード\"\"\"\n print(\"\\n=== Loading MASt3R Model ===\")\n\n if '/kaggle/working/mast3r' not in sys.path:\n sys.path.insert(0, '/kaggle/working/mast3r')\n if '/kaggle/working/mast3r/dust3r' not in sys.path:\n sys.path.insert(0, '/kaggle/working/mast3r/dust3r')\n\n from dust3r.model import AsymmetricCroCo3DStereo\n\n try:\n print(f\"Attempting to load: {Config.MAST3R_WEIGHTS}\")\n model = AsymmetricCroCo3DStereo.from_pretrained(Config.MAST3R_WEIGHTS).to(device)\n print(\"✓ Loaded MASt3R model\")\n except Exception as e:\n print(f\"⚠️ Failed to load MASt3R: {e}\")\n print(f\"Trying DUSt3R instead: {Config.DUST3R_WEIGHTS}\")\n model = AsymmetricCroCo3DStereo.from_pretrained(Config.DUST3R_WEIGHTS).to(device)\n print(\"✓ Loaded DUSt3R model as fallback\")\n\n model.eval()\n print(f\"✓ Model loaded on {device}\")\n return model\n\n# =====================================================================\n# CELL 9: DINO Pair Selection (REPLACES ASMK)\n# =====================================================================\ndef load_torch_image(fname, device):\n \"\"\"Load image as torch tensor\"\"\"\n import torchvision.transforms as T\n\n img = Image.open(fname).convert('RGB')\n transform = T.Compose([\n T.ToTensor(),\n ])\n return transform(img).unsqueeze(0).to(device)\n\ndef extract_dino_global(image_paths, model_path, device):\n \"\"\"Extract DINO global descriptors with memory management\"\"\"\n print(\"\\n=== Extracting DINO Global Features ===\")\n print(\"Initial memory state:\")\n get_memory_info()\n\n processor = AutoImageProcessor.from_pretrained(model_path)\n model = AutoModel.from_pretrained(model_path).eval().to(device)\n\n global_descs = []\n batch_size = 4 # Small batch to save memory\n\n for i in tqdm(range(0, len(image_paths), batch_size), desc=\"DINO extraction\"):\n batch_paths = image_paths[i:i+batch_size]\n batch_imgs = []\n\n for img_path in batch_paths:\n img = load_torch_image(img_path, device)\n batch_imgs.append(img)\n\n batch_tensor = torch.cat(batch_imgs, dim=0)\n\n with torch.no_grad():\n inputs = processor(images=batch_tensor, return_tensors=\"pt\", do_rescale=False).to(device)\n outputs = model(**inputs)\n desc = F.normalize(outputs.last_hidden_state[:, 1:].max(dim=1)[0], dim=1, p=2)\n global_descs.append(desc.cpu())\n\n # Clear batch memory\n del batch_tensor, inputs, outputs, desc\n clear_memory()\n\n global_descs = torch.cat(global_descs, dim=0)\n\n del model, processor\n clear_memory()\n\n print(\"After DINO extraction:\")\n get_memory_info()\n\n return global_descs\n\ndef build_topk_pairs(global_feats, k, device):\n \"\"\"Build top-k similar pairs from global features\"\"\"\n g = global_feats.to(device)\n sim = g @ g.T\n sim.fill_diagonal_(-1)\n\n N = sim.size(0)\n k = min(k, N - 1)\n\n topk_indices = torch.topk(sim, k, dim=1).indices.cpu()\n\n pairs = []\n for i in range(N):\n for j in topk_indices[i]:\n j = j.item()\n if i < j:\n pairs.append((i, j))\n\n # Remove duplicates\n pairs = list(set(pairs))\n\n return pairs\n\ndef select_diverse_pairs(pairs, max_pairs, num_images):\n \"\"\"\n Select diverse pairs to ensure good image coverage\n \"\"\"\n import random\n random.seed(42)\n\n if len(pairs) <= max_pairs:\n return pairs\n\n print(f\"Selecting {max_pairs} diverse pairs from {len(pairs)} candidates...\")\n\n # Count how many times each image appears in pairs\n image_counts = {i: 0 for i in range(num_images)}\n for i, j in pairs:\n image_counts[i] += 1\n image_counts[j] += 1\n\n # Sort pairs by: prefer pairs with less-connected images\n def pair_score(pair):\n i, j = pair\n return image_counts[i] + image_counts[j]\n\n pairs_scored = [(pair, pair_score(pair)) for pair in pairs]\n pairs_scored.sort(key=lambda x: x[1])\n\n # Select pairs greedily to maximize coverage\n selected = []\n selected_images = set()\n\n # Phase 1: Select pairs that add new images\n for pair, score in pairs_scored:\n if len(selected) >= max_pairs:\n break\n i, j = pair\n if i not in selected_images or j not in selected_images:\n selected.append(pair)\n selected_images.add(i)\n selected_images.add(j)\n\n # Phase 2: Fill remaining slots\n if len(selected) < max_pairs:\n remaining = [p for p, s in pairs_scored if p not in selected]\n random.shuffle(remaining)\n selected.extend(remaining[:max_pairs - len(selected)])\n\n print(f\"Selected pairs cover {len(selected_images)} / {num_images} images ({100*len(selected_images)/num_images:.1f}%)\")\n\n return selected\n\ndef get_image_pairs_dino(image_paths, max_pairs=None):\n \"\"\"DINO-based pair selection\"\"\"\n device = Config.DEVICE\n\n # DINO global features\n global_feats = extract_dino_global(image_paths, Config.DINO_MODEL, device)\n pairs = build_topk_pairs(global_feats, Config.GLOBAL_TOPK, device)\n\n print(f\"Initial pairs from DINO: {len(pairs)}\")\n\n # Apply intelligent pair selection if limit specified\n if max_pairs and len(pairs) > max_pairs:\n pairs = select_diverse_pairs(pairs, max_pairs, len(image_paths))\n\n return pairs\n\n# =====================================================================\n# CELL 10: MASt3R Reconstruction\n# =====================================================================\ndef run_mast3r_pairs(model, image_paths, pairs, device, batch_size=1, max_pairs=None):\n \"\"\"Run MASt3R on selected pairs with memory management\"\"\"\n print(\"\\n=== Running MASt3R Reconstruction ===\")\n print(\"Initial memory state:\")\n get_memory_info()\n\n from dust3r.inference import inference\n from dust3r.cloud_opt import global_aligner, GlobalAlignerMode\n from dust3r.utils.image import load_images\n\n # Limit number of pairs if specified\n if max_pairs and len(pairs) > max_pairs:\n print(f\"Limiting pairs from {len(pairs)} to {max_pairs}\")\n step = max(1, len(pairs) // max_pairs)\n pairs = pairs[::step][:max_pairs]\n\n print(f\"Processing {len(pairs)} pairs...\")\n\n # Load images in smaller size\n print(f\"Loading {len(image_paths)} images at {Config.IMAGE_SIZE}x{Config.IMAGE_SIZE}...\")\n images = load_images(image_paths, size=Config.IMAGE_SIZE)\n\n print(f\"Loaded {len(images)} images\")\n print(\"After loading images:\")\n get_memory_info()\n\n # Create all image pairs\n print(f\"Creating {len(pairs)} image pairs...\")\n mast3r_pairs = []\n for idx1, idx2 in tqdm(pairs, desc=\"Preparing pairs\"):\n mast3r_pairs.append((images[idx1], images[idx2]))\n\n print(f\"Running MASt3R inference on {len(mast3r_pairs)} pairs...\")\n\n # Run inference\n output = inference(mast3r_pairs, model, device, batch_size=batch_size, verbose=True)\n\n del mast3r_pairs\n clear_memory()\n\n print(\"✓ MASt3R inference complete\")\n print(\"After inference:\")\n get_memory_info()\n\n # Global alignment\n print(\"Running global alignment...\")\n scene = global_aligner(\n output,\n device=device,\n mode=GlobalAlignerMode.PointCloudOptimizer\n )\n\n del output\n clear_memory()\n\n print(\"Computing global alignment...\")\n loss = scene.compute_global_alignment(\n init=\"mst\",\n niter=50, # Reduced iterations\n schedule='cosine',\n lr=0.01\n )\n\n print(f\"✓ Global alignment complete (final loss: {loss:.6f})\")\n print(\"Final memory state:\")\n get_memory_info()\n\n return scene, images\n\n\n\n","metadata":{"trusted":true,"id":"OWJEB1oQTKyD","outputId":"fa123527-2b15-4fa5-8d3c-c830ccc43365","execution":{"iopub.status.busy":"2026-02-01T07:13:59.804995Z","iopub.execute_input":"2026-02-01T07:13:59.805314Z","iopub.status.idle":"2026-02-01T07:17:30.292541Z","shell.execute_reply.started":"2026-02-01T07:13:59.805287Z","shell.execute_reply":"2026-02-01T07:17:30.291526Z"}},"outputs":[{"name":"stdout","text":"✓ np: 1.26.4 - /usr/local/lib/python3.12/dist-packages/numpy/__init__.py\nVersion: 1.26.4\nVersion 3.1, 31 March 2009\n Version 3, 29 June 2007\n 5. Conveying Modified Source Versions.\n 14. Revised Versions of this License.\n✓ roma is installed\nCloning MASt3R repository...\nCloning into '/kaggle/working/mast3r'...\nremote: Enumerating objects: 269, done.\u001b[K\nremote: Counting objects: 100% (170/170), done.\u001b[K\nremote: Compressing objects: 100% (61/61), done.\u001b[K\nremote: Total 269 (delta 115), reused 109 (delta 109), pack-reused 99 (from 1)\u001b[K\nReceiving objects: 100% (269/269), 3.59 MiB | 10.19 MiB/s, done.\nResolving deltas: 100% (151/151), done.\nSubmodule 'dust3r' (https://github.com/naver/dust3r) registered for path 'dust3r'\nCloning into '/kaggle/working/mast3r/dust3r'...\nremote: Enumerating objects: 611, done. \nremote: Total 611 (delta 0), reused 0 (delta 0), pack-reused 611 (from 1) \nReceiving objects: 100% (611/611), 756.60 KiB | 2.65 MiB/s, done.\nResolving deltas: 100% (355/355), done.\nSubmodule path 'dust3r': checked out '3cc8c88c413bb9e34c41db0e0eef99c2ee010b12'\nSubmodule 'croco' (https://github.com/naver/croco) registered for path 'dust3r/croco'\nCloning into '/kaggle/working/mast3r/dust3r/croco'...\nremote: Enumerating objects: 198, done. \nremote: Counting objects: 100% (87/87), done. \nremote: Compressing objects: 100% (54/54), done. \nremote: Total 198 (delta 54), reused 33 (delta 33), pack-reused 111 (from 1) \nReceiving objects: 100% (198/198), 403.93 KiB | 2.28 MiB/s, done.\nResolving deltas: 100% (94/94), done.\nSubmodule path 'dust3r/croco': checked out 'd7de0705845239092414480bd829228723bf20de'\n✓ MASt3R cloned\n✓ DUSt3R already exists\nWarning, cannot find cuda-compiled version of RoPE2D, using a slow pytorch version instead\n✓ dust3r.model imported successfully\nCloning CroCo repository...\nCloning into '/kaggle/working/mast3r/croco'...\nremote: Enumerating objects: 198, done.\u001b[K\nremote: Counting objects: 100% (87/87), done.\u001b[K\nremote: Compressing objects: 100% (54/54), done.\u001b[K\nremote: Total 198 (delta 54), reused 33 (delta 33), pack-reused 111 (from 1)\u001b[K\nReceiving objects: 100% (198/198), 403.93 KiB | 2.28 MiB/s, done.\nResolving deltas: 100% (94/94), done.\n✓ CroCo cloned\n\n======================================================================\nSTEP: Clone Gaussian Splatting\n======================================================================\n✓ Cloned\nInstalling /kaggle/working/gaussian-splatting/submodules/diff-gaussian-rasterization...\nProcessing ./gaussian-splatting/submodules/diff-gaussian-rasterization\n Preparing metadata (setup.py): started\n Preparing metadata (setup.py): finished with status 'done'\nBuilding wheels for collected packages: diff_gaussian_rasterization\n Building wheel for diff_gaussian_rasterization (setup.py): started\n Building wheel for diff_gaussian_rasterization (setup.py): finished with status 'done'\n Created wheel for diff_gaussian_rasterization: filename=diff_gaussian_rasterization-0.0.0-cp312-cp312-linux_x86_64.whl size=3457000 sha256=6cd46ff194764b233975640619619a19903f620f43daf5be8cc747241a4889e6\n Stored in directory: /root/.cache/pip/wheels/ba/99/d3/014520068aca8c2e8bdc358ca774581380cadb65788559b3ea\nSuccessfully built diff_gaussian_rasterization\nInstalling collected packages: diff_gaussian_rasterization\nSuccessfully installed diff_gaussian_rasterization-0.0.0\nInstalling /kaggle/working/gaussian-splatting/submodules/simple-knn...\nProcessing ./gaussian-splatting/submodules/simple-knn\n Preparing metadata (setup.py): started\n Preparing metadata (setup.py): finished with status 'done'\nBuilding wheels for collected packages: simple_knn\n Building wheel for simple_knn (setup.py): started\n Building wheel for simple_knn (setup.py): finished with status 'done'\n Created wheel for simple_knn: filename=simple_knn-0.0.0-cp312-cp312-linux_x86_64.whl size=3209861 sha256=b31752d43022c3690e53aeaa1243d28f1988ce457b1d18ae38d78dcfc0315cc5\n Stored in directory: /root/.cache/pip/wheels/ca/30/df/7f4f362d12edead48c699acde5962cbb06ca05033b9d970934\nSuccessfully built simple_knn\nInstalling collected packages: simple_knn\nSuccessfully installed simple_knn-0.0.0\n✓ Custom CUDA modules installed.\n✓ np: 1.26.4 - /usr/local/lib/python3.12/dist-packages/numpy/__init__.py\nVersion: 1.26.4\nVersion 3.1, 31 March 2009\n Version 3, 29 June 2007\n 5. Conveying Modified Source Versions.\n 14. Revised Versions of this License.\n","output_type":"stream"},{"name":"stderr","text":"2026-02-01 07:17:19.374015: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\nWARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nE0000 00:00:1769930239.543895 141 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\nE0000 00:00:1769930239.599685 141 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\nW0000 00:00:1769930240.004523 141 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1769930240.004565 141 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1769930240.004568 141 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1769930240.004570 141 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n","output_type":"stream"}],"execution_count":1},{"cell_type":"markdown","source":"# process2","metadata":{}},{"cell_type":"code","source":"\nimport struct\nimport numpy as np\nfrom pathlib import Path\n\ndef rotmat_to_qvec(R):\n \"\"\"回転行列をクォータニオンに変換\"\"\"\n R = np.asarray(R, dtype=np.float64)\n trace = np.trace(R)\n\n if trace > 0:\n s = 0.5 / np.sqrt(trace + 1.0)\n w = 0.25 / s\n x = (R[2, 1] - R[1, 2]) * s\n y = (R[0, 2] - R[2, 0]) * s\n z = (R[1, 0] - R[0, 1]) * s\n elif R[0, 0] > R[1, 1] and R[0, 0] > R[2, 2]:\n s = 2.0 * np.sqrt(1.0 + R[0, 0] - R[1, 1] - R[2, 2])\n w = (R[2, 1] - R[1, 2]) / s\n x = 0.25 * s\n y = (R[0, 1] + R[1, 0]) / s\n z = (R[0, 2] + R[2, 0]) / s\n elif R[1, 1] > R[2, 2]:\n s = 2.0 * np.sqrt(1.0 + R[1, 1] - R[0, 0] - R[2, 2])\n w = (R[0, 2] - R[2, 0]) / s\n x = (R[0, 1] + R[1, 0]) / s\n y = 0.25 * s\n z = (R[1, 2] + R[2, 1]) / s\n else:\n s = 2.0 * np.sqrt(1.0 + R[2, 2] - R[0, 0] - R[1, 1])\n w = (R[1, 0] - R[0, 1]) / s\n x = (R[0, 2] + R[2, 0]) / s\n y = (R[1, 2] + R[2, 1]) / s\n z = 0.25 * s\n\n qvec = np.array([w, x, y, z], dtype=np.float64)\n qvec = qvec / np.linalg.norm(qvec)\n\n return qvec\n\n\ndef write_cameras_binary(cameras_dict, image_size, output_file):\n \"\"\"\n cameras.binを出力（PINHOLEモデル使用）\n \"\"\"\n width, height = image_size\n num_cameras = len(cameras_dict)\n\n # COLMAP camera models\n PINHOLE = 1 # 🔧 SIMPLE_PINHOLE (0) から PINHOLE (1) に変更\n\n with open(output_file, 'wb') as f:\n f.write(struct.pack('Q', num_cameras))\n\n for camera_id, (img_id, cam_params) in enumerate(cameras_dict.items(), start=1):\n focal = cam_params['focal']\n\n # PINHOLEの場合: fx, fy, cx, cy\n #fx = fy = focal # 等方性カメラを仮定\n \n #new settiing 2026/01/26\n if isinstance(focal, (tuple, list)):\n fx, fy = focal\n else:\n fx = fy = focal\n \n\n # Principal pointを取得（存在しない場合は中心）\n if 'pp' in cam_params:\n pp = cam_params['pp']\n cx = float(pp[0])\n cy = float(pp[1])\n else:\n cx = width / 2.0\n cy = height / 2.0\n\n # camera_id\n f.write(struct.pack('I', camera_id))\n # model_id (PINHOLE = 1)\n f.write(struct.pack('i', PINHOLE))\n # width\n f.write(struct.pack('Q', width))\n # height\n f.write(struct.pack('Q', height))\n # params: fx, fy, cx, cy (4パラメータ)\n f.write(struct.pack('d', fx))\n f.write(struct.pack('d', fy))\n f.write(struct.pack('d', cx))\n f.write(struct.pack('d', cy))\n\n print(f\"COLMAP cameras.bin saved to {output_file}\")\n\n\ndef write_images_binary(cameras_dict, output_file):\n \"\"\"images.binを出力\"\"\"\n num_images = len(cameras_dict)\n\n with open(output_file, 'wb') as f:\n f.write(struct.pack('Q', num_images))\n\n for image_id, (img_id, cam_params) in enumerate(cameras_dict.items(), start=1):\n R = cam_params['rotation']\n quat = rotmat_to_qvec(R)\n t = cam_params['translation']\n camera_id = image_id\n\n f.write(struct.pack('I', image_id))\n for q in quat:\n f.write(struct.pack('d', q))\n for ti in t:\n f.write(struct.pack('d', ti))\n f.write(struct.pack('I', camera_id))\n\n name_bytes = img_id.encode('utf-8') + b'\\x00'\n f.write(name_bytes)\n f.write(struct.pack('Q', 0))\n\n print(f\"COLMAP images.bin saved to {output_file}\")\n\n\ndef write_points3D_binary(pts3d, confidence, output_file):\n \"\"\"points3D.binを出力\"\"\"\n num_points = len(pts3d)\n\n with open(output_file, 'wb') as f:\n f.write(struct.pack('Q', num_points))\n\n for point_id, pt in enumerate(pts3d, start=1):\n x, y, z = pt\n\n f.write(struct.pack('Q', point_id))\n f.write(struct.pack('d', x))\n f.write(struct.pack('d', y))\n f.write(struct.pack('d', z))\n\n # RGB (グレー)\n f.write(struct.pack('B', 128))\n f.write(struct.pack('B', 128))\n f.write(struct.pack('B', 128))\n\n # error\n if confidence is not None and point_id <= len(confidence):\n error = 1.0 / max(confidence[point_id-1], 0.001)\n else:\n error = 1.0\n f.write(struct.pack('d', error))\n\n # track_length\n f.write(struct.pack('Q', 0))\n\n print(f\"COLMAP points3D.bin saved to {output_file}\")\n\n\ndef export_colmap_binary(cameras_dict, pts3d, confidence, image_size, output_dir):\n \"\"\"COLMAPバイナリファイルを出力\"\"\"\n output_path = Path(output_dir)\n output_path.mkdir(parents=True, exist_ok=True)\n\n write_cameras_binary(\n cameras_dict,\n image_size,\n output_path / 'cameras.bin'\n )\n\n write_images_binary(\n cameras_dict,\n output_path / 'images.bin'\n )\n\n write_points3D_binary(\n pts3d,\n confidence,\n output_path / 'points3D.bin'\n )\n\n print(f\"\\nCOLMAP binary files exported to {output_dir}/\")\n print(f\" - cameras.bin: {len(cameras_dict)} cameras (PINHOLE model)\")\n print(f\" - images.bin: {len(cameras_dict)} images\")\n print(f\" - points3D.bin: {len(pts3d)} points\")\n\n\n# =====================================================================\n# CELL 11: Camera Parameter Extraction (REVISED 2026/01/26)\n# =====================================================================\ndef extract_camera_params_process2(scene, image_paths, conf_threshold=1.5):\n \"\"\"\n Extracts camera parameters and 3D points from the scene (FIXED: proper fx, fy handling).\n \"\"\"\n print(\"\\n=== Extracting Camera Parameters ===\")\n\n cameras_dict = {}\n all_pts3d = []\n all_confidence = []\n\n try:\n # Attempt to get camera poses\n if hasattr(scene, 'get_im_poses'):\n poses = scene.get_im_poses()\n elif hasattr(scene, 'im_poses'):\n poses = scene.im_poses\n else:\n poses = None\n\n # Attempt to get focal lengths\n if hasattr(scene, 'get_focals'):\n focals = scene.get_focals()\n elif hasattr(scene, 'im_focals'):\n focals = scene.im_focals\n else:\n focals = None\n\n # Attempt to get principal points\n if hasattr(scene, 'get_principal_points'):\n pps = scene.get_principal_points()\n elif hasattr(scene, 'im_pp'):\n pps = scene.im_pp\n else:\n pps = None\n except Exception as e:\n print(f\"⚠️ Error getting camera parameters: {e}\")\n poses = None\n focals = None\n pps = None\n\n # [Important] MASt3R internal processing size\n mast3r_size = 224.0\n\n n_images = min(len(poses) if poses is not None else len(image_paths), len(image_paths))\n\n for idx in range(n_images):\n img_name = os.path.basename(image_paths[idx])\n\n try:\n # Get original image dimensions\n img = Image.open(image_paths[idx])\n W, H = img.size\n img.close()\n\n # Calculate scaling ratio\n scale = W / mast3r_size\n\n # Get Pose (Convert camera-to-world to world-to-camera)\n if poses is not None and idx < len(poses):\n pose_c2w = poses[idx]\n if isinstance(pose_c2w, torch.Tensor):\n pose_c2w = pose_c2w.detach().cpu().numpy()\n if not isinstance(pose_c2w, np.ndarray) or pose_c2w.shape != (4, 4):\n pose_c2w = np.eye(4)\n\n # Invert to get world-to-camera pose\n pose = np.linalg.inv(pose_c2w)\n else:\n pose = np.eye(4)\n\n # 🔧 FIX: Get and scale focal length (handle both isotropic and anisotropic)\n if focals is not None and idx < len(focals):\n focal_mast3r = focals[idx]\n if isinstance(focal_mast3r, torch.Tensor):\n focal_mast3r = focal_mast3r.detach().cpu()\n\n # Check if isotropic (fx = fy) or anisotropic (fx ≠ fy)\n if focals.shape[1] == 1:\n # Isotropic camera (fx = fy)\n focal_val = float(focal_mast3r) if focal_mast3r.numel() == 1 else float(focal_mast3r[0])\n fx = fy = focal_val * scale\n else:\n # Anisotropic camera (fx ≠ fy)\n fx = float(focal_mast3r[0]) * scale\n fy = float(focal_mast3r[1]) * scale\n else:\n # Default fallback\n fx = fy = 1000.0\n\n # Get and scale principal point\n if pps is not None and idx < len(pps):\n pp_mast3r = pps[idx]\n if isinstance(pp_mast3r, torch.Tensor):\n pp_mast3r = pp_mast3r.detach().cpu().numpy()\n\n # 🔧 Apply scaling\n pp = pp_mast3r * scale\n else:\n pp = np.array([W / 2.0, H / 2.0])\n\n # 🔧 FIX: Store camera parameters with focal as tuple (fx, fy)\n cameras_dict[img_name] = {\n 'focal': (fx, fy), # ← FIXED: Store as tuple\n 'pp': pp,\n 'pose': pose,\n 'rotation': pose[:3, :3],\n 'translation': pose[:3, 3],\n 'width': W,\n 'height': H\n }\n\n # Debugging info (First image only)\n if idx == 0:\n print(f\"\\nExample camera 0:\")\n print(f\" Original size: {W}x{H}\")\n print(f\" MASt3R size: {mast3r_size}\")\n print(f\" Scale factor: {scale:.3f}\")\n print(f\" focals.shape: {focals.shape}\")\n if focals.shape[1] == 1:\n print(f\" MASt3R focal: {focal_val:.2f}\")\n print(f\" Scaled focal: fx = fy = {fx:.2f}\")\n else:\n print(f\" MASt3R focals: fx={float(focal_mast3r[0]):.2f}, fy={float(focal_mast3r[1]):.2f}\")\n print(f\" Scaled focals: fx={fx:.2f}, fy={fy:.2f}\")\n print(f\" MASt3R pp: [{pp_mast3r[0]:.2f}, {pp_mast3r[1]:.2f}]\")\n print(f\" Scaled pp: [{pp[0]:.2f}, {pp[1]:.2f}]\")\n\n # Extract 3D points\n if hasattr(scene, 'im_pts3d') and idx < len(scene.im_pts3d):\n pts3d_img = scene.im_pts3d[idx]\n elif hasattr(scene, 'get_pts3d'):\n pts3d_all = scene.get_pts3d()\n pts3d_img = pts3d_all[idx] if idx < len(pts3d_all) else None\n else:\n pts3d_img = None\n\n # Extract confidence scores\n if hasattr(scene, 'im_conf') and idx < len(scene.im_conf):\n conf_img = scene.im_conf[idx]\n elif hasattr(scene, 'get_conf'):\n conf_all = scene.get_conf()\n conf_img = conf_all[idx] if idx < len(conf_all) else None\n else:\n conf_img = None\n\n # Process 3D points and confidence\n if pts3d_img is not None:\n if isinstance(pts3d_img, torch.Tensor):\n pts3d_img = pts3d_img.detach().cpu().numpy()\n\n pts3d_flat = pts3d_img.reshape(-1, 3) if pts3d_img.ndim == 3 else pts3d_img\n all_pts3d.append(pts3d_flat)\n\n if conf_img is not None:\n if isinstance(conf_img, (list, torch.Tensor)):\n conf_img = np.array(conf_img) if isinstance(conf_img, list) else conf_img.detach().cpu().numpy()\n\n conf_flat = conf_img.reshape(-1) if conf_img.ndim > 1 else conf_img\n \n if len(conf_flat) != len(pts3d_flat):\n conf_flat = np.ones(len(pts3d_flat))\n \n all_confidence.append(conf_flat)\n else:\n all_confidence.append(np.ones(len(pts3d_flat)))\n\n except Exception as e:\n print(f\"⚠️ Error processing image {idx} ({img_name}): {e}\")\n # Fallback to default values with scaling applied\n img = Image.open(image_paths[idx])\n W, H = img.size\n img.close()\n\n cameras_dict[img_name] = {\n 'focal': (1000.0 * (W / mast3r_size), 1000.0 * (W / mast3r_size)), # ← FIXED: Tuple\n 'pp': np.array([W / 2.0, H / 2.0]),\n 'pose': np.eye(4),\n 'rotation': np.eye(3),\n 'translation': np.zeros(3),\n 'width': W,\n 'height': H\n }\n continue\n\n # Consolidate all 3D points\n if all_pts3d:\n pts3d = np.vstack(all_pts3d)\n confidence = np.concatenate(all_confidence)\n else:\n pts3d = np.zeros((0, 3))\n confidence = np.zeros(0)\n\n print(f\"✓ Extracted parameters for {len(cameras_dict)} cameras\")\n print(f\"✓ Total 3D points: {len(pts3d)}\")\n\n # Filter points by confidence\n if len(confidence) > 0:\n valid_mask = confidence > conf_threshold\n pts3d = pts3d[valid_mask]\n confidence = confidence[valid_mask]\n print(f\"✓ Points after confidence filtering (>{conf_threshold}): {len(pts3d)}\")\n\n return cameras_dict, pts3d, confidence\n\n# =====================================================================\n# Complete Color Extraction for Process2 (newly defined 2026/01/26)\n# =====================================================================\n\nimport numpy as np\nfrom PIL import Image\nimport struct\nfrom pathlib import Path\n\n# =====================================================================\n# STEP 1: Color Extraction Function\n# =====================================================================\n\ndef extract_colors_from_images(scene, image_paths, pts3d, confidence, conf_threshold=1.5):\n \"\"\"\n Extract colors from images that match the filtered pts3d.\n \n This matches Traditional method's color extraction.\n \n Args:\n scene: MASt3R scene object\n image_paths: List of image file paths\n pts3d: (N, 3) filtered 3D points (after confidence filtering)\n confidence: (N,) filtered confidence scores\n conf_threshold: Confidence threshold used for filtering\n \n Returns:\n colors: (N, 3) RGB colors [0-255] matching pts3d\n \"\"\"\n print(\"\\n=== Extracting Colors from Images ===\")\n \n # Get all 3D points BEFORE filtering (to match with colors)\n all_pts3d = []\n for idx in range(len(image_paths)):\n if hasattr(scene, 'im_pts3d') and idx < len(scene.im_pts3d):\n pts3d_img = scene.im_pts3d[idx]\n elif hasattr(scene, 'get_pts3d'):\n pts3d_all = scene.get_pts3d()\n pts3d_img = pts3d_all[idx] if idx < len(pts3d_all) else None\n else:\n pts3d_img = None\n \n if pts3d_img is not None:\n if isinstance(pts3d_img, torch.Tensor):\n pts3d_img = pts3d_img.detach().cpu().numpy()\n pts3d_flat = pts3d_img.reshape(-1, 3) if pts3d_img.ndim == 3 else pts3d_img\n all_pts3d.append(pts3d_flat)\n \n # Get dimensions from first image\n first_img = Image.open(image_paths[0])\n W_orig, H_orig = first_img.size\n first_img.close()\n \n # MASt3R uses 224x224 internally\n mast3r_size = 224\n \n # Extract colors from all images\n print(f\"Extracting colors from {len(image_paths)} images...\")\n all_colors = []\n \n for idx, img_path in enumerate(image_paths):\n # Open and resize image to MASt3R size (224x224)\n img = Image.open(img_path)\n img_resized = img.resize((mast3r_size, mast3r_size), Image.BILINEAR)\n img_array = np.array(img_resized) # Shape: (224, 224, 3)\n img.close()\n \n # Reshape to (224*224, 3) to match point order\n colors_flat = img_array.reshape(-1, 3)\n all_colors.append(colors_flat)\n \n if idx == 0:\n print(f\" Example image 0:\")\n print(f\" Original size: {W_orig}x{H_orig}\")\n print(f\" Resized to: {mast3r_size}x{mast3r_size}\")\n print(f\" Colors shape: {colors_flat.shape}\")\n \n # Stack all colors\n colors_all = np.vstack(all_colors) # Shape: (N_total, 3)\n print(f\"✓ Total colors extracted: {len(colors_all):,}\")\n \n # Get confidence for all points (before filtering)\n all_conf = []\n for idx in range(len(image_paths)):\n if hasattr(scene, 'im_conf') and idx < len(scene.im_conf):\n conf_img = scene.im_conf[idx]\n elif hasattr(scene, 'get_conf'):\n conf_all = scene.get_conf()\n conf_img = conf_all[idx] if idx < len(conf_all) else None\n else:\n conf_img = None\n \n if conf_img is not None:\n if isinstance(conf_img, torch.Tensor):\n conf_img = conf_img.detach().cpu().numpy()\n conf_flat = conf_img.reshape(-1) if conf_img.ndim > 1 else conf_img\n else:\n conf_flat = np.ones(len(all_pts3d[idx]))\n \n all_conf.append(conf_flat)\n \n conf_all = np.concatenate(all_conf)\n \n # Apply THE SAME filtering as pts3d\n valid_mask = conf_all > conf_threshold\n colors_filtered = colors_all[valid_mask]\n \n print(f\"✓ Colors after confidence filtering (>{conf_threshold}): {len(colors_filtered):,}\")\n \n # Verify shapes match\n if len(colors_filtered) != len(pts3d):\n print(f\"⚠️ WARNING: Color count ({len(colors_filtered)}) != Point count ({len(pts3d)})\")\n print(f\" Adjusting to match...\")\n min_len = min(len(colors_filtered), len(pts3d))\n colors_filtered = colors_filtered[:min_len]\n else:\n print(f\"✓ Colors match points: {len(colors_filtered):,} colors for {len(pts3d):,} points\")\n \n # Verify colors are diverse\n unique_colors = len(np.unique(colors_filtered, axis=0))\n print(f\"✓ Unique colors: {unique_colors:,}\")\n \n if unique_colors < 100:\n print(f\"⚠️ WARNING: Very few unique colors!\")\n else:\n print(f\"✓ Good color diversity\")\n \n return colors_filtered\n\n\n# =====================================================================\n# STEP 2: Write points3D.bin with Colors\n# =====================================================================\n\ndef write_points3D_binary_with_colors(pts3d, confidence, colors, output_file):\n \"\"\"\n Export points3D.bin with actual colors.\n \n Args:\n pts3d: (N, 3) array of 3D points\n confidence: (N,) array of confidence scores\n colors: (N, 3) array of RGB colors [0-255]\n output_file: Path to output file\n \"\"\"\n num_points = len(pts3d)\n\n with open(output_file, 'wb') as f:\n f.write(struct.pack('Q', num_points))\n\n for point_id, (pt, color) in enumerate(zip(pts3d, colors), start=1):\n x, y, z = pt\n\n f.write(struct.pack('Q', point_id))\n f.write(struct.pack('d', x))\n f.write(struct.pack('d', y))\n f.write(struct.pack('d', z))\n\n # RGB Color (ACTUAL colors now!)\n r = int(np.clip(color[0], 0, 255))\n g = int(np.clip(color[1], 0, 255))\n b = int(np.clip(color[2], 0, 255))\n \n f.write(struct.pack('B', r))\n f.write(struct.pack('B', g))\n f.write(struct.pack('B', b))\n\n # Error estimation\n if confidence is not None and point_id <= len(confidence):\n error = 1.0 / max(confidence[point_id-1], 0.001)\n else:\n error = 1.0\n f.write(struct.pack('d', error))\n\n # track_length (Set to 0)\n f.write(struct.pack('Q', 0))\n\n print(f\"COLMAP points3D.bin saved to {output_file}\")\n print(f\" ✓ With actual RGB colors from images!\")\n\n\n# =====================================================================\n# STEP 3: Export with Colors\n# =====================================================================\n\ndef export_colmap_binary_with_colors(cameras_dict, pts3d, confidence, colors, \n image_size, output_dir):\n \"\"\"\n Export COLMAP binary files with actual colors.\n \n Args:\n cameras_dict: Dictionary of camera parameters\n pts3d: (N, 3) filtered 3D points\n confidence: (N,) filtered confidence scores\n colors: (N, 3) RGB colors [0-255]\n image_size: (width, height) tuple\n output_dir: Output directory path\n \"\"\"\n output_path = Path(output_dir)\n output_path.mkdir(parents=True, exist_ok=True)\n\n # Write cameras.bin (same as before)\n write_cameras_binary(\n cameras_dict,\n image_size,\n output_path / 'cameras.bin'\n )\n\n # Write images.bin (same as before)\n write_images_binary(\n cameras_dict,\n output_path / 'images.bin'\n )\n\n # Write points3D.bin WITH COLORS (NEW!)\n write_points3D_binary_with_colors(\n pts3d,\n confidence,\n colors, # ← Actual colors!\n output_path / 'points3D.bin'\n )\n\n print(f\"\\n✓ COLMAP binary files exported to {output_dir}/\")\n print(f\" - cameras.bin: {len(cameras_dict)} cameras (PINHOLE model)\")\n print(f\" - images.bin: {len(cameras_dict)} images\")\n print(f\" - points3D.bin: {len(pts3d)} points WITH COLORS\")\n\n\n# =====================================================================\n# STEP 4: Complete Workflow\n# =====================================================================\n\ndef create_process2_with_colors(scene, image_paths, output_dir, conf_threshold=1.5):\n \"\"\"\n Complete workflow: Process2 with color extraction.\n \n Usage:\n create_process2_with_colors(\n scene, \n image_paths, \n '/kaggle/working/output/sparse_process2_with_colors/0',\n conf_threshold=1.5\n )\n \"\"\"\n print(\"=\"*80)\n print(\"CREATING PROCESS2 COLMAP WITH COLORS\")\n print(\"=\"*80)\n \n # Step 1: Extract camera parameters and points\n cameras_dict, pts3d, confidence = extract_camera_params_process2(\n scene, image_paths, conf_threshold=conf_threshold\n )\n \n print(f\"\\n✓ Extracted:\")\n print(f\" - {len(cameras_dict)} cameras\")\n print(f\" - {len(pts3d):,} 3D points\")\n \n # Step 2: Extract colors (NEW!)\n colors = extract_colors_from_images(\n scene, image_paths, pts3d, confidence, conf_threshold\n )\n \n # Step 3: Get image size\n img = Image.open(image_paths[0])\n image_size = img.size\n img.close()\n \n # Step 4: Export with colors\n export_colmap_binary_with_colors(\n cameras_dict, pts3d, confidence, colors,\n image_size, output_dir\n )\n \n print(\"\\n\" + \"=\"*80)\n print(\"✓ COMPLETE!\")\n print(\"=\"*80)\n print(\"\\nOutput directory:\", output_dir)\n print(\"\\nNext steps:\")\n print(\"1. Train 3DGS with this reconstruction\")\n print(\"2. Compare quality with gray Process2 and Traditional\")\n print(\"3. Check if colors improve geometry convergence\")\n \n return cameras_dict, pts3d, confidence, colors","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-02-01T07:17:30.294772Z","iopub.execute_input":"2026-02-01T07:17:30.295353Z","iopub.status.idle":"2026-02-01T07:17:30.354448Z","shell.execute_reply.started":"2026-02-01T07:17:30.295323Z","shell.execute_reply":"2026-02-01T07:17:30.353539Z"}},"outputs":[],"execution_count":2},{"cell_type":"markdown","source":"# end of process2\n# process1","metadata":{}},{"cell_type":"code","source":"\n# ===== Traditional Method: extract_colmap_data =====\ndef extract_colmap_data_traditional(scene, image_paths, max_points=1000000):\n \"\"\"\n Traditional Method: Extract COLMAP-compatible data from a MASt3R scene.\n (Derived from dino-mast3r-gs-kg-34oo.ipynb)\n \"\"\"\n print(\"\\n=== [TRADITIONAL] Extracting COLMAP-compatible data ===\")\n\n # Extract point cloud\n pts_all = scene.get_pts3d()\n print(f\"pts_all type: {type(pts_all)}\")\n\n if isinstance(pts_all, list):\n print(f\"pts_all is a list with {len(pts_all)} elements\")\n if len(pts_all) > 0:\n print(f\"First element type: {type(pts_all[0])}\")\n if hasattr(pts_all[0], 'shape'):\n print(f\"First element shape: {pts_all[0].shape}\")\n\n pts_all = torch.stack([p if isinstance(p, torch.Tensor) else torch.tensor(p)\n for p in pts_all])\n print(f\"pts_all shape after conversion: {pts_all.shape}\")\n\n if len(pts_all.shape) == 4:\n print(f\"Found batched point cloud: {pts_all.shape}\")\n B, H, W, _ = pts_all.shape\n pts3d = pts_all.reshape(-1, 3).detach().cpu().numpy()\n\n # Extract colors\n colors = []\n for img_path in image_paths:\n img = Image.open(img_path).resize((W, H))\n colors.append(np.array(img))\n colors = np.stack(colors).reshape(-1, 3) / 255.0\n else:\n pts3d = pts_all.detach().cpu().numpy() if isinstance(pts_all, torch.Tensor) else pts_all\n colors = np.ones((len(pts3d), 3)) * 0.5\n\n print(f\"✓ Extracted {len(pts3d)} 3D points from {len(image_paths)} images\")\n\n # Downsample points\n if len(pts3d) > max_points:\n print(f\"\\n⚠ Downsampling from {len(pts3d)} to {max_points} points...\")\n valid_mask = ~(np.isnan(pts3d).any(axis=1) | np.isinf(pts3d).any(axis=1))\n pts3d_valid = pts3d[valid_mask]\n colors_valid = colors[valid_mask]\n \n # Count excluded points\n num_excluded = len(pts3d_valid) - max_points\n \n indices = np.random.choice(len(pts3d_valid), size=max_points, replace=False)\n pts3d = pts3d_valid[indices]\n colors = colors_valid[indices]\n print(f\"✓ Downsampled to {len(pts3d)} points\")\n print(f\"⚠ Excluded {num_excluded} points due to max_points limit\")\n\n # Extract camera parameters\n print(\"Extracting camera parameters...\")\n\n # [Important] Convert camera-to-world (C2W) to world-to-camera (W2C)\n poses_c2w = scene.get_im_poses().detach().cpu().numpy()\n print(f\"Retrieved camera-to-world poses: shape {poses_c2w.shape}\")\n\n poses = []\n for i, pose_c2w in enumerate(poses_c2w):\n pose_w2c = np.linalg.inv(pose_c2w)\n poses.append(pose_w2c)\n poses = np.array(poses)\n print(\"Converted to world-to-camera poses for COLMAP\")\n\n focals = scene.get_focals().detach().cpu().numpy()\n pp = scene.get_principal_points().detach().cpu().numpy()\n print(f\"Focals shape: {focals.shape}\")\n print(f\"Principal points shape: {pp.shape}\")\n\n mast3r_size = 224.0\n\n cameras = []\n for i, img_path in enumerate(image_paths):\n img = Image.open(img_path)\n W, H = img.size\n scale = W / mast3r_size\n\n if focals.shape[1] == 1:\n focal_mast3r = float(focals[i, 0])\n fx = fy = focal_mast3r * scale\n else:\n fx = float(focals[i, 0]) * scale\n fy = float(focals[i, 1]) * scale\n\n cx = float(pp[i, 0]) * scale\n cy = float(pp[i, 1]) * scale\n\n camera = {\n 'camera_id': i + 1,\n 'model': 'PINHOLE',\n 'width': W,\n 'height': H,\n 'params': [fx, fy, cx, cy]\n }\n cameras.append(camera)\n\n if i == 0:\n print(f\"\\nExample camera 0:\")\n print(f\" Image size: {W}x{H}\")\n print(f\" MASt3R focal: {focal_mast3r:.2f}, pp: ({pp[i,0]:.2f}, {pp[i,1]:.2f})\")\n print(f\" Scaled fx={fx:.2f}, fy={fy:.2f}, cx={cx:.2f}, cy={cy:.2f}\")\n print(f\" Pose (first row): {poses[i][0]}\")\n\n print(f\"\\n✓ Extracted {len(cameras)} cameras and {len(poses)} poses\")\n\n pts3d = pts3d.reshape(-1, 3)\n colors = np.ones((len(pts3d), 3)) * 0.5\n \n return pts3d, colors, cameras, poses\n\n\n# ===== Traditional Method: rotmat2qvec =====\ndef rotmat2qvec_traditional(R):\n \"\"\"Traditional Method: Convert rotation matrix to quaternion.\"\"\"\n R = np.asarray(R, dtype=np.float64)\n trace = np.trace(R)\n\n if trace > 0:\n s = 0.5 / np.sqrt(trace + 1.0)\n w = 0.25 / s\n x = (R[2, 1] - R[1, 2]) * s\n y = (R[0, 2] - R[2, 0]) * s\n z = (R[1, 0] - R[0, 1]) * s\n elif R[0, 0] > R[1, 1] and R[0, 0] > R[2, 2]:\n s = 2.0 * np.sqrt(1.0 + R[0, 0] - R[1, 1] - R[2, 2])\n w = (R[2, 1] - R[1, 2]) / s\n x = 0.25 * s\n y = (R[0, 1] + R[1, 0]) / s\n z = (R[0, 2] + R[2, 0]) / s\n elif R[1, 1] > R[2, 2]:\n s = 2.0 * np.sqrt(1.0 + R[1, 1] - R[0, 0] - R[2, 2])\n w = (R[0, 2] - R[2, 0]) / s\n x = (R[0, 1] + R[1, 0]) / s\n y = 0.25 * s\n z = (R[1, 2] + R[2, 1]) / s\n else:\n s = 2.0 * np.sqrt(1.0 + R[2, 2] - R[0, 0] - R[1, 1])\n w = (R[1, 0] - R[0, 1]) / s\n x = (R[0, 2] + R[2, 0]) / s\n y = (R[1, 2] + R[2, 1]) / s\n z = 0.25 * s\n\n qvec = np.array([w, x, y, z], dtype=np.float64)\n qvec = qvec / np.linalg.norm(qvec)\n\n return qvec\n\n\n# ===== Traditional Method: Save Functions =====\ndef write_cameras_binary_traditional(cameras, output_file):\n \"\"\"Traditional Method: Write cameras.bin.\"\"\"\n with open(output_file, 'wb') as f:\n f.write(struct.pack('Q', len(cameras)))\n\n for i, cam in enumerate(cameras):\n camera_id = cam.get('camera_id', i + 1)\n model_id = 1 # PINHOLE\n width = cam['width']\n height = cam['height']\n params = cam['params']\n\n f.write(struct.pack('i', camera_id))\n f.write(struct.pack('i', model_id))\n f.write(struct.pack('Q', width))\n f.write(struct.pack('Q', height))\n\n for param in params[:4]:\n f.write(struct.pack('d', param))\n\n\ndef write_images_binary_traditional(image_paths, cameras, poses, output_file):\n \"\"\"Traditional Method: Write images.bin.\"\"\"\n with open(output_file, 'wb') as f:\n f.write(struct.pack('Q', len(image_paths)))\n\n for i, (img_path, pose) in enumerate(zip(image_paths, poses)):\n image_id = i + 1\n camera_id = cameras[i].get('camera_id', i + 1)\n image_name = os.path.basename(img_path)\n\n R = pose[:3, :3]\n t = pose[:3, 3]\n qvec = rotmat2qvec_traditional(R)\n tvec = t\n\n f.write(struct.pack('i', image_id))\n for q in qvec:\n f.write(struct.pack('d', float(q)))\n for tv in tvec:\n f.write(struct.pack('d', float(tv)))\n f.write(struct.pack('i', camera_id))\n f.write(image_name.encode('utf-8') + b'\\x00')\n f.write(struct.pack('Q', 0))\n\n\ndef write_points3d_binary_traditional(pts3d, colors, output_file):\n \"\"\"Traditional Method: Write points3D.bin.\"\"\"\n valid_indices = []\n invalid_count = 0\n \n for i, pt in enumerate(pts3d):\n if not (np.isnan(pt).any() or np.isinf(pt).any()):\n valid_indices.append(i)\n else:\n invalid_count += 1\n\n with open(output_file, 'wb') as f:\n f.write(struct.pack('Q', len(valid_indices)))\n\n for idx, point_id in enumerate(valid_indices):\n pt = pts3d[point_id]\n color = colors[point_id]\n\n f.write(struct.pack('Q', point_id))\n for coord in np.asarray(pt).ravel(): \n f.write(struct.pack('d', float(coord)))\n\n col_int = (color * 255).astype(np.uint8)\n for c in col_int:\n f.write(struct.pack('B', int(c)))\n\n f.write(struct.pack('d', 0.0))\n f.write(struct.pack('Q', 0))\n\n if invalid_count > 0:\n print(f\" ⚠ Excluded {invalid_count} invalid points (NaN/Inf)\")\n\n return len(valid_indices)\n\n\ndef save_colmap_reconstruction_traditional(pts3d, colors, cameras, poses, image_paths, output_dir):\n \"\"\"Traditional Method: Save COLMAP reconstruction.\"\"\"\n print(\"\\n=== [TRADITIONAL] Saving COLMAP reconstruction ===\")\n\n sparse_dir = Path(output_dir) / 'sparse_traditional' / '0'\n sparse_dir.mkdir(parents=True, exist_ok=True)\n\n write_cameras_binary_traditional(cameras, sparse_dir / 'cameras.bin')\n print(f\" ✓ Wrote {len(cameras)} cameras\")\n\n write_images_binary_traditional(image_paths, cameras, poses, sparse_dir / 'images.bin')\n print(f\" ✓ Wrote {len(image_paths)} images\")\n\n num_points = write_points3d_binary_traditional(pts3d, colors, sparse_dir / 'points3D.bin')\n print(f\" ✓ Wrote {num_points} 3D points\")\n\n print(f\"\\n✓ Traditional COLMAP reconstruction saved to {sparse_dir}\")\n\n return sparse_dir","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-02-01T07:17:30.355449Z","iopub.execute_input":"2026-02-01T07:17:30.355724Z","iopub.status.idle":"2026-02-01T07:17:30.385258Z","shell.execute_reply.started":"2026-02-01T07:17:30.355701Z","shell.execute_reply":"2026-02-01T07:17:30.384421Z"}},"outputs":[],"execution_count":3},{"cell_type":"markdown","source":"# end of process1","metadata":{}},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# =====================================================================\n# CELL 20: Traditional Method Functions (for comparison)\n# =====================================================================\nimport struct\nimport numpy as np\nfrom pathlib import Path\n\n# ===== 従来法: extract_colmap_data =====\ndef extract_colmap_data_traditional(scene, image_paths, max_points=1000000):\n \"\"\"\n 従来法: MASt3Rシーンから COLMAP互換データを抽出\n （dino-mast3r-gs-kg-34oo.ipynb からの抽出）\n \"\"\"\n print(\"\\n=== [TRADITIONAL] Extracting COLMAP-compatible data ===\")\n\n # Extract point cloud\n pts_all = scene.get_pts3d()\n print(f\"pts_all type: {type(pts_all)}\")\n\n if isinstance(pts_all, list):\n print(f\"pts_all is a list with {len(pts_all)} elements\")\n if len(pts_all) > 0:\n print(f\"First element type: {type(pts_all[0])}\")\n if hasattr(pts_all[0], 'shape'):\n print(f\"First element shape: {pts_all[0].shape}\")\n\n pts_all = torch.stack([p if isinstance(p, torch.Tensor) else torch.tensor(p)\n for p in pts_all])\n print(f\"pts_all shape after conversion: {pts_all.shape}\")\n\n if len(pts_all.shape) == 4:\n print(f\"Found batched point cloud: {pts_all.shape}\")\n B, H, W, _ = pts_all.shape\n pts3d = pts_all.reshape(-1, 3).detach().cpu().numpy()\n\n # Extract colors\n colors = []\n for img_path in image_paths:\n img = Image.open(img_path).resize((W, H))\n colors.append(np.array(img))\n colors = np.stack(colors).reshape(-1, 3) / 255.0\n else:\n pts3d = pts_all.detach().cpu().numpy() if isinstance(pts_all, torch.Tensor) else pts_all\n colors = np.ones((len(pts3d), 3)) * 0.5\n\n print(f\"✓ Extracted {len(pts3d)} 3D points from {len(image_paths)} images\")\n\n # Downsample points\n if len(pts3d) > max_points:\n print(f\"\\n⚠ Downsampling from {len(pts3d)} to {max_points} points...\")\n valid_mask = ~(np.isnan(pts3d).any(axis=1) | np.isinf(pts3d).any(axis=1))\n pts3d_valid = pts3d[valid_mask]\n colors_valid = colors[valid_mask]\n indices = np.random.choice(len(pts3d_valid), size=max_points, replace=False)\n pts3d = pts3d_valid[indices]\n colors = colors_valid[indices]\n print(f\"✓ Downsampled to {len(pts3d)} points\")\n\n # Extract camera parameters\n print(\"Extracting camera parameters...\")\n\n # 【重要】camera-to-world を world-to-camera に変換\n poses_c2w = scene.get_im_poses().detach().cpu().numpy()\n print(f\"Retrieved camera-to-world poses: shape {poses_c2w.shape}\")\n\n poses = []\n for i, pose_c2w in enumerate(poses_c2w):\n pose_w2c = np.linalg.inv(pose_c2w)\n poses.append(pose_w2c)\n poses = np.array(poses)\n print(f\"Converted to world-to-camera poses for COLMAP\")\n\n focals = scene.get_focals().detach().cpu().numpy()\n pp = scene.get_principal_points().detach().cpu().numpy()\n print(f\"Focals shape: {focals.shape}\")\n print(f\"Principal points shape: {pp.shape}\")\n\n mast3r_size = 224.0\n\n cameras = []\n for i, img_path in enumerate(image_paths):\n img = Image.open(img_path)\n W, H = img.size\n scale = W / mast3r_size\n\n if focals.shape[1] == 1:\n focal_mast3r = float(focals[i, 0])\n fx = fy = focal_mast3r * scale\n else:\n fx = float(focals[i, 0]) * scale\n fy = float(focals[i, 1]) * scale\n\n cx = float(pp[i, 0]) * scale\n cy = float(pp[i, 1]) * scale\n\n camera = {\n 'camera_id': i + 1,\n 'model': 'PINHOLE',\n 'width': W,\n 'height': H,\n 'params': [fx, fy, cx, cy]\n }\n cameras.append(camera)\n\n if i == 0:\n print(f\"\\nExample camera 0:\")\n print(f\" Image size: {W}x{H}\")\n print(f\" MASt3R focal: {focal_mast3r:.2f}, pp: ({pp[i,0]:.2f}, {pp[i,1]:.2f})\")\n print(f\" Scaled fx={fx:.2f}, fy={fy:.2f}, cx={cx:.2f}, cy={cy:.2f}\")\n print(f\" Pose (first row): {poses[i][0]}\")\n\n print(f\"\\n✓ Extracted {len(cameras)} cameras and {len(poses)} poses\")\n\n return pts3d, colors, cameras, poses\n\n\n# ===== 従来法: rotmat2qvec =====\ndef rotmat2qvec_traditional(R):\n \"\"\"従来法: 回転行列をクォータニオンに変換\"\"\"\n R = np.asarray(R, dtype=np.float64)\n trace = np.trace(R)\n\n if trace > 0:\n s = 0.5 / np.sqrt(trace + 1.0)\n w = 0.25 / s\n x = (R[2, 1] - R[1, 2]) * s\n y = (R[0, 2] - R[2, 0]) * s\n z = (R[1, 0] - R[0, 1]) * s\n elif R[0, 0] > R[1, 1] and R[0, 0] > R[2, 2]:\n s = 2.0 * np.sqrt(1.0 + R[0, 0] - R[1, 1] - R[2, 2])\n w = (R[2, 1] - R[1, 2]) / s\n x = 0.25 * s\n y = (R[0, 1] + R[1, 0]) / s\n z = (R[0, 2] + R[2, 0]) / s\n elif R[1, 1] > R[2, 2]:\n s = 2.0 * np.sqrt(1.0 + R[1, 1] - R[0, 0] - R[2, 2])\n w = (R[0, 2] - R[2, 0]) / s\n x = (R[0, 1] + R[1, 0]) / s\n y = 0.25 * s\n z = (R[1, 2] + R[2, 1]) / s\n else:\n s = 2.0 * np.sqrt(1.0 + R[2, 2] - R[0, 0] - R[1, 1])\n w = (R[1, 0] - R[0, 1]) / s\n x = (R[0, 2] + R[2, 0]) / s\n y = (R[1, 2] + R[2, 1]) / s\n z = 0.25 * s\n\n qvec = np.array([w, x, y, z], dtype=np.float64)\n qvec = qvec / np.linalg.norm(qvec)\n\n return qvec\n\n\n# ===== 従来法: save関数群 =====\ndef write_cameras_binary_traditional(cameras, output_file):\n \"\"\"従来法: cameras.binを書き込み\"\"\"\n with open(output_file, 'wb') as f:\n f.write(struct.pack('Q', len(cameras)))\n\n for i, cam in enumerate(cameras):\n camera_id = cam.get('camera_id', i + 1)\n model_id = 1 # PINHOLE\n width = cam['width']\n height = cam['height']\n params = cam['params']\n\n f.write(struct.pack('i', camera_id))\n f.write(struct.pack('i', model_id))\n f.write(struct.pack('Q', width))\n f.write(struct.pack('Q', height))\n\n for param in params[:4]:\n f.write(struct.pack('d', param))\n\n\ndef write_images_binary_traditional(image_paths, cameras, poses, output_file):\n \"\"\"従来法: images.binを書き込み\"\"\"\n with open(output_file, 'wb') as f:\n f.write(struct.pack('Q', len(image_paths)))\n\n for i, (img_path, pose) in enumerate(zip(image_paths, poses)):\n image_id = i + 1\n camera_id = cameras[i].get('camera_id', i + 1)\n image_name = os.path.basename(img_path)\n\n R = pose[:3, :3]\n t = pose[:3, 3]\n qvec = rotmat2qvec_traditional(R)\n tvec = t\n\n f.write(struct.pack('i', image_id))\n for q in qvec:\n f.write(struct.pack('d', float(q)))\n for tv in tvec:\n f.write(struct.pack('d', float(tv)))\n f.write(struct.pack('i', camera_id))\n f.write(image_name.encode('utf-8') + b'\\x00')\n f.write(struct.pack('Q', 0))\n\n\ndef write_points3d_binary_traditional(pts3d, colors, output_file):\n \"\"\"従来法: points3D.binを書き込み\"\"\"\n valid_indices = []\n for i, pt in enumerate(pts3d):\n if not (np.isnan(pt).any() or np.isinf(pt).any()):\n valid_indices.append(i)\n\n with open(output_file, 'wb') as f:\n f.write(struct.pack('Q', len(valid_indices)))\n\n for idx, point_id in enumerate(valid_indices):\n pt = pts3d[point_id]\n color = colors[point_id]\n\n f.write(struct.pack('Q', point_id))\n for coord in pt:\n f.write(struct.pack('d', float(coord)))\n\n col_int = (color * 255).astype(np.uint8)\n for c in col_int:\n f.write(struct.pack('B', int(c)))\n\n f.write(struct.pack('d', 0.0))\n f.write(struct.pack('Q', 0))\n\n return len(valid_indices)\n\n\ndef save_colmap_reconstruction_traditional(pts3d, colors, cameras, poses, image_paths, output_dir):\n \"\"\"従来法: COLMAP再構成を保存\"\"\"\n print(\"\\n=== [TRADITIONAL] Saving COLMAP reconstruction ===\")\n\n sparse_dir = Path(output_dir) / 'sparse_traditional' / '0'\n sparse_dir.mkdir(parents=True, exist_ok=True)\n\n write_cameras_binary_traditional(cameras, sparse_dir / 'cameras.bin')\n print(f\" ✓ Wrote {len(cameras)} cameras\")\n\n write_images_binary_traditional(image_paths, cameras, poses, sparse_dir / 'images.bin')\n print(f\" ✓ Wrote {len(image_paths)} images\")\n\n num_points = write_points3d_binary_traditional(pts3d, colors, sparse_dir / 'points3D.bin')\n print(f\" ✓ Wrote {num_points} 3D points\")\n\n print(f\"\\n✓ Traditional COLMAP reconstruction saved to {sparse_dir}\")\n\n return sparse_dir","metadata":{"id":"kIrrlZXQEkSA","trusted":true,"execution":{"iopub.status.busy":"2026-02-01T07:17:30.386183Z","iopub.execute_input":"2026-02-01T07:17:30.386424Z","iopub.status.idle":"2026-02-01T07:17:30.415173Z","shell.execute_reply.started":"2026-02-01T07:17:30.386403Z","shell.execute_reply":"2026-02-01T07:17:30.414280Z"}},"outputs":[],"execution_count":4},{"cell_type":"code","source":"# =====================================================================\n# CELL 21: Convert BIN to CSV for Easy Comparison\n# =====================================================================\nimport pandas as pd\nimport struct\n\ndef bin_to_csv_cameras(bin_file, csv_file):\n \"\"\"cameras.bin → CSV\"\"\"\n data = []\n with open(bin_file, 'rb') as f:\n num_cameras = struct.unpack('Q', f.read(8))[0]\n for _ in range(num_cameras):\n camera_id = struct.unpack('i', f.read(4))[0]\n model_id = struct.unpack('i', f.read(4))[0]\n width = struct.unpack('Q', f.read(8))[0]\n height = struct.unpack('Q', f.read(8))[0]\n\n # PINHOLE: 4 params\n if model_id == 1:\n params = struct.unpack('dddd', f.read(32))\n # SIMPLE_PINHOLE: 3 params\n else:\n params = struct.unpack('ddd', f.read(24))\n\n data.append({\n 'camera_id': camera_id,\n 'model_id': model_id,\n 'width': width,\n 'height': height,\n 'fx': params[0] if len(params) >= 1 else None,\n 'fy': params[1] if len(params) >= 2 else params[0] if len(params) == 1 else None,\n 'cx': params[2] if len(params) >= 3 else None,\n 'cy': params[3] if len(params) >= 4 else None\n })\n\n df = pd.DataFrame(data)\n df.to_csv(csv_file, index=False)\n print(f\"✓ Cameras CSV saved: {csv_file}\")\n return df\n\n\ndef bin_to_csv_images(bin_file, csv_file):\n \"\"\"images.bin → CSV\"\"\"\n data = []\n with open(bin_file, 'rb') as f:\n num_images = struct.unpack('Q', f.read(8))[0]\n for _ in range(num_images):\n image_id = struct.unpack('i', f.read(4))[0]\n qvec = struct.unpack('dddd', f.read(32))\n tvec = struct.unpack('ddd', f.read(24))\n camera_id = struct.unpack('i', f.read(4))[0]\n\n name = b''\n while True:\n char = f.read(1)\n if char == b'\\x00':\n break\n name += char\n name = name.decode('utf-8')\n\n num_points2D = struct.unpack('Q', f.read(8))[0]\n f.read(num_points2D * 24)\n\n data.append({\n 'image_id': image_id,\n 'qw': qvec[0],\n 'qx': qvec[1],\n 'qy': qvec[2],\n 'qz': qvec[3],\n 'tx': tvec[0],\n 'ty': tvec[1],\n 'tz': tvec[2],\n 'camera_id': camera_id,\n 'name': name\n })\n\n df = pd.DataFrame(data)\n df.to_csv(csv_file, index=False)\n print(f\"✓ Images CSV saved: {csv_file}\")\n return df\n\n\ndef bin_to_csv_points3d(bin_file, csv_file, max_rows=10000):\n \"\"\"points3D.bin → CSV (サンプリング)\"\"\"\n data = []\n with open(bin_file, 'rb') as f:\n num_points = struct.unpack('Q', f.read(8))[0]\n\n # サンプリング間隔を計算\n step = max(1, num_points // max_rows)\n\n for i in range(num_points):\n point_id = struct.unpack('Q', f.read(8))[0]\n xyz = struct.unpack('ddd', f.read(24))\n rgb = struct.unpack('BBB', f.read(3))\n error = struct.unpack('d', f.read(8))[0]\n track_length = struct.unpack('Q', f.read(8))[0]\n f.read(track_length * 8)\n\n # サンプリング\n if i % step == 0:\n data.append({\n 'point_id': point_id,\n 'x': xyz[0],\n 'y': xyz[1],\n 'z': xyz[2],\n 'r': rgb[0],\n 'g': rgb[1],\n 'b': rgb[2],\n 'error': error\n })\n\n df = pd.DataFrame(data)\n df.to_csv(csv_file, index=False)\n print(f\"✓ Points3D CSV saved: {csv_file} (sampled {len(df)} / {num_points} points)\")\n return df\n\n\ndef convert_colmap_bins_to_csv(sparse_dir, output_prefix):\n \"\"\"全BINファイルをCSVに変換\"\"\"\n print(f\"\\n=== Converting {sparse_dir} to CSV ===\")\n\n cameras_df = bin_to_csv_cameras(\n os.path.join(sparse_dir, 'cameras.bin'),\n f\"{output_prefix}_cameras.csv\"\n )\n\n images_df = bin_to_csv_images(\n os.path.join(sparse_dir, 'images.bin'),\n f\"{output_prefix}_images.csv\"\n )\n\n points_df = bin_to_csv_points3d(\n os.path.join(sparse_dir, 'points3D.bin'),\n f\"{output_prefix}_points3d.csv\",\n max_rows=10000\n )\n\n return cameras_df, images_df, points_df","metadata":{"id":"c7A05pXLFt2E","trusted":true,"execution":{"iopub.status.busy":"2026-02-01T07:17:30.417176Z","iopub.execute_input":"2026-02-01T07:17:30.417533Z","iopub.status.idle":"2026-02-01T07:17:30.435058Z","shell.execute_reply.started":"2026-02-01T07:17:30.417502Z","shell.execute_reply":"2026-02-01T07:17:30.434392Z"}},"outputs":[],"execution_count":5},{"cell_type":"code","source":"# =====================================================================\n# CELL 22: Comparison Function\n# =====================================================================\n\ndef compare_extraction_methods(scene, image_paths, output_dir, conf_threshold=0.5, max_points=100000):\n \"\"\"\n 新方式と従来法の両方でCOLMAP形式を出力し、比較する\n\n Args:\n scene: MASt3Rのシーンオブジェクト\n image_paths: 画像パスのリスト\n output_dir: 出力ディレクトリ\n conf_threshold: 信頼度閾値（新方式用）\n max_points: 最大点数（従来法用）\n\n Returns:\n dict: 比較結果の辞書\n \"\"\"\n print(\"\\n\" + \"=\"*70)\n print(\"COMPARISON: New vs Traditional Extraction Methods\")\n print(\"=\"*70)\n\n # ===== METHOD 1: 新方式 (extract_camera_params_process2) =====\n print(\"\\n--- METHOD 1: Current Implementation (extract_camera_params_process2) ---\")\n\n cameras_dict_new, pts3d_new, confidence_new = extract_camera_params_process2(\n scene=scene,\n image_paths=image_paths,\n conf_threshold=conf_threshold\n )\n\n # 画像サイズを取得\n first_img = Image.open(image_paths[0])\n image_size = (first_img.width, first_img.height)\n first_img.close()\n\n # 新方式のBIN保存\n sparse_dir_new = os.path.join(output_dir, \"sparse_new/0\")\n os.makedirs(sparse_dir_new, exist_ok=True)\n\n export_colmap_binary(\n cameras_dict=cameras_dict_new,\n pts3d=pts3d_new,\n confidence=confidence_new,\n image_size=image_size,\n output_dir=sparse_dir_new\n )\n\n \n # ===== METHOD 2: 従来法 (extract_colmap_data_traditional) =====\n print(\"\\n--- METHOD 2: Traditional Implementation (extract_colmap_data) ---\")\n\n pts3d_trad, colors_trad, cameras_trad, poses_trad = extract_colmap_data_traditional(\n scene=scene,\n image_paths=image_paths,\n max_points=max_points\n )\n\n # 従来法のBIN保存\n sparse_dir_trad = save_colmap_reconstruction_traditional(\n pts3d=pts3d_trad,\n colors=colors_trad,\n cameras=cameras_trad,\n poses=poses_trad,\n image_paths=image_paths,\n output_dir=output_dir\n )\n\n # ===== CSVに変換 =====\n print(\"\\n\" + \"=\"*70)\n print(\"Converting to CSV for comparison\")\n print(\"=\"*70)\n\n csv_prefix_new = os.path.join(output_dir, \"comparison_new\")\n csv_prefix_trad = os.path.join(output_dir, \"comparison_traditional\")\n\n cam_new, img_new, pts_new = convert_colmap_bins_to_csv(\n sparse_dir_new,\n csv_prefix_new\n )\n\n cam_trad, img_trad, pts_trad = convert_colmap_bins_to_csv(\n str(sparse_dir_trad),\n csv_prefix_trad\n )\n\n # ===== 比較サマリー =====\n print(\"\\n\" + \"=\"*70)\n print(\"COMPARISON SUMMARY\")\n print(\"=\"*70)\n\n comparison_results = {\n 'cameras': {\n 'new_count': len(cam_new),\n 'trad_count': len(cam_trad),\n 'new_focal': float(cam_new.iloc[0]['fx']) if len(cam_new) > 0 else None,\n 'trad_focal': float(cam_trad.iloc[0]['fx']) if len(cam_trad) > 0 else None,\n },\n 'images': {\n 'new_count': len(img_new),\n 'trad_count': len(img_trad),\n 'new_tvec': [float(img_new.iloc[0]['tx']), float(img_new.iloc[0]['ty']), float(img_new.iloc[0]['tz'])] if len(img_new) > 0 else None,\n 'trad_tvec': [float(img_trad.iloc[0]['tx']), float(img_trad.iloc[0]['ty']), float(img_trad.iloc[0]['tz'])] if len(img_trad) > 0 else None,\n },\n 'points': {\n 'new_count': len(pts_new),\n 'trad_count': len(pts_trad),\n 'new_center': [float(pts_new['x'].mean()), float(pts_new['y'].mean()), float(pts_new['z'].mean())] if len(pts_new) > 0 else None,\n 'trad_center': [float(pts_trad['x'].mean()), float(pts_trad['y'].mean()), float(pts_trad['z'].mean())] if len(pts_trad) > 0 else None,\n }\n }\n\n # 結果を表示\n print(\"\\nCAMERAS:\")\n print(f\" New method: {comparison_results['cameras']['new_count']} cameras\")\n print(f\" Traditional method: {comparison_results['cameras']['trad_count']} cameras\")\n if comparison_results['cameras']['new_focal'] and comparison_results['cameras']['trad_focal']:\n print(f\"\\n Sample focal lengths:\")\n print(f\" New: fx={comparison_results['cameras']['new_focal']:.2f}\")\n print(f\" Traditional: fx={comparison_results['cameras']['trad_focal']:.2f}\")\n focal_diff = abs(comparison_results['cameras']['new_focal'] - comparison_results['cameras']['trad_focal'])\n print(f\" Difference: {focal_diff:.2f}\")\n\n print(\"\\nIMAGES:\")\n print(f\" New method: {comparison_results['images']['new_count']} images\")\n print(f\" Traditional method: {comparison_results['images']['trad_count']} images\")\n if comparison_results['images']['new_tvec'] and comparison_results['images']['trad_tvec']:\n print(f\"\\n Sample translation (first image):\")\n print(f\" New: {comparison_results['images']['new_tvec']}\")\n print(f\" Traditional: {comparison_results['images']['trad_tvec']}\")\n tvec_diff = np.linalg.norm(\n np.array(comparison_results['images']['new_tvec']) -\n np.array(comparison_results['images']['trad_tvec'])\n )\n print(f\" Distance: {tvec_diff:.3f}\")\n\n print(\"\\nPOINTS3D:\")\n print(f\" New method: {comparison_results['points']['new_count']} points (sampled)\")\n print(f\" Traditional method: {comparison_results['points']['trad_count']} points (sampled)\")\n if comparison_results['points']['new_center'] and comparison_results['points']['trad_center']:\n print(f\"\\n Center of points:\")\n print(f\" New: {comparison_results['points']['new_center']}\")\n print(f\" Traditional: {comparison_results['points']['trad_center']}\")\n center_diff = np.linalg.norm(\n np.array(comparison_results['points']['new_center']) -\n np.array(comparison_results['points']['trad_center'])\n )\n print(f\" Distance: {center_diff:.3f}\")\n\n print(\"\\n\" + \"=\"*70)\n print(\"CSV FILES SAVED:\")\n print(\"=\"*70)\n print(f\" New method:\")\n print(f\" - {csv_prefix_new}_cameras.csv\")\n print(f\" - {csv_prefix_new}_images.csv\")\n print(f\" - {csv_prefix_new}_points3d.csv\")\n print(f\" Traditional method:\")\n print(f\" - {csv_prefix_trad}_cameras.csv\")\n print(f\" - {csv_prefix_trad}_images.csv\")\n print(f\" - {csv_prefix_trad}_points3d.csv\")\n\n print(\"\\n✓ Comparison complete! Review CSV files for detailed analysis.\")\n\n return comparison_results","metadata":{"id":"SN1a_CbWEkIg","trusted":true,"execution":{"iopub.status.busy":"2026-02-01T07:17:30.435858Z","iopub.execute_input":"2026-02-01T07:17:30.436059Z","iopub.status.idle":"2026-02-01T07:17:30.455587Z","shell.execute_reply.started":"2026-02-01T07:17:30.436040Z","shell.execute_reply":"2026-02-01T07:17:30.454623Z"}},"outputs":[],"execution_count":6},{"cell_type":"code","source":"","metadata":{"id":"lHdqGcsaDLfb","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# =====================================================================\n# CELL 13: Gaussian Splatting Runner\n# =====================================================================\ndef run_gaussian_splatting(source_dir, output_dir, iterations=30000):\n \"\"\"Gaussian Splattingを実行\"\"\"\n print(\"\\n=== Running Gaussian Splatting ===\")\n\n os.makedirs(output_dir, exist_ok=True)\n\n cmd = [\n \"python\", \"/kaggle/working/gaussian-splatting/train.py\",\n \"-s\", source_dir,\n \"-m\", output_dir,\n \"--iterations\", str(iterations),\n \"--eval\"\n ]\n\n print(f\"Command: {' '.join(cmd)}\")\n print(f\" Source: {source_dir}\")\n print(f\" Output: {output_dir}\")\n\n result = subprocess.run(cmd, capture_output=False, text=True)\n\n if result.returncode == 0:\n print(f\"\\n✓ Gaussian Splatting complete\")\n\n point_cloud_dir = os.path.join(output_dir, \"point_cloud\")\n if os.path.exists(point_cloud_dir):\n print(f\"\\n✓ Point cloud directory found: {point_cloud_dir}\")\n\n for item in sorted(os.listdir(point_cloud_dir)):\n item_path = os.path.join(point_cloud_dir, item)\n if os.path.isdir(item_path) and item.startswith(\"iteration_\"):\n ply_file = os.path.join(item_path, \"point_cloud.ply\")\n if os.path.exists(ply_file):\n file_size = os.path.getsize(ply_file) / (1024 * 1024)\n print(f\" ✓ {item}/point_cloud.ply ({file_size:.2f} MB)\")\n else:\n print(f\"\\n✗ Gaussian Splatting failed with return code {result.returncode}\")\n\n return output_dir","metadata":{"id":"o0n2RL3Ep5_Y","trusted":true,"execution":{"iopub.status.busy":"2026-02-01T07:17:30.456737Z","iopub.execute_input":"2026-02-01T07:17:30.457095Z","iopub.status.idle":"2026-02-01T07:17:30.473231Z","shell.execute_reply.started":"2026-02-01T07:17:30.457059Z","shell.execute_reply":"2026-02-01T07:17:30.472331Z"}},"outputs":[],"execution_count":7},{"cell_type":"code","source":"# =====================================================================\n# FIXED: Main Pipeline with Color Extraction\n# =====================================================================\n\ndef main_pipeline(image_dir, output_dir, square_size=1024, iterations=30000,\n max_images=200, max_pairs=100, max_points=500000,\n conf_threshold=1.001, preprocess_mode='none'):\n \"\"\"メインパイプライン（色抽出対応版）\"\"\"\n\n\n # STEP 0: Image Preprocessing\n if preprocess_mode == 'biplet':\n print(\"=\"*70)\n print(\"STEP 0: Image Preprocessing (Biplet Crops)\")\n print(\"=\"*70)\n\n temp_biplet_dir = os.path.join(output_dir, \"temp_biplet\")\n biplet_dir = normalize_image_sizes_biplet(image_dir, temp_biplet_dir, size=square_size)\n\n images_dir = os.path.join(output_dir, \"images\")\n os.makedirs(images_dir, exist_ok=True)\n\n biplet_suffixes = ['_left', '_right', '_top', '_bottom']\n copied_count = 0\n\n for img_file in os.listdir(temp_biplet_dir):\n if any(suffix in img_file for suffix in biplet_suffixes):\n src = os.path.join(temp_biplet_dir, img_file)\n dst = os.path.join(images_dir, img_file)\n shutil.copy2(src, dst)\n copied_count += 1\n\n print(f\"✓ Copied {copied_count} biplet images to {images_dir}\")\n\n original_images_dir = os.path.join(output_dir, \"original_images\")\n os.makedirs(original_images_dir, exist_ok=True)\n\n original_count = 0\n valid_extensions = ('.jpg', '.jpeg', '.png', '.bmp')\n for img_file in os.listdir(image_dir):\n if img_file.lower().endswith(valid_extensions):\n src = os.path.join(image_dir, img_file)\n dst = os.path.join(original_images_dir, img_file)\n shutil.copy2(src, dst)\n original_count += 1\n\n print(f\"✓ Saved {original_count} original images to {original_images_dir}\")\n shutil.rmtree(temp_biplet_dir)\n image_dir = images_dir\n clear_memory()\n else:\n images_dir = os.path.join(output_dir, \"images\")\n if not os.path.exists(images_dir):\n print(\"=\"*70)\n print(\"STEP 0: Copying images to output directory\")\n print(\"=\"*70)\n shutil.copytree(image_dir, images_dir)\n print(f\"✓ Copied images to {images_dir}\")\n image_dir = images_dir\n\n # STEP 1: Loading Images\n print(\"\\n\" + \"=\"*70)\n print(\"STEP 1: Loading and Preparing Images\")\n print(\"=\"*70)\n\n image_paths = load_images_from_directory(image_dir, max_images=max_images)\n print(f\"Loaded {len(image_paths)} images\")\n clear_memory()\n\n # STEP 2: Image Pair Selection (DINO)\n print(\"\\n\" + \"=\"*70)\n print(\"STEP 2: Image Pair Selection (DINO)\")\n print(\"=\"*70)\n\n max_pairs = min(max_pairs, 50)\n pairs = get_image_pairs_dino(image_paths, max_pairs=max_pairs)\n print(f\"Selected {len(pairs)} image pairs\")\n clear_memory()\n\n # STEP 3: MASt3R 3D Reconstruction\n print(\"\\n\" + \"=\"*70)\n print(\"STEP 3: MASt3R 3D Reconstruction\")\n print(\"=\"*70)\n\n device = Config.DEVICE\n model = load_mast3r_model(device)\n scene, mast3r_images = run_mast3r_pairs(model, image_paths, pairs, device)\n\n del model\n clear_memory()\n # ...\n\n # STEP 4: Converting to COLMAP (CELL 11/12使用)\n print(\"\\n\" + \"=\"*70)\n print(\"STEP 4: Converting to COLMAP (PINHOLE) WITH COLORS\")\n print(\"=\"*70)\n\n # 🔧 FIX: カメラパラメータと色を一度に抽出\n cameras_dict, pts3d, confidence = extract_camera_params_process2(\n scene=scene,\n image_paths=image_paths,\n conf_threshold=conf_threshold\n )\n \n print(f\"Extracted {len(cameras_dict)} cameras with conf >= {conf_threshold}\")\n print(f\"Extracted {len(pts3d):,} 3D points\")\n\n # 🔧 FIX: 色を抽出\n print(\"\\n[Extracting colors from images...]\")\n colors = extract_colors_from_images(\n scene=scene,\n image_paths=image_paths,\n pts3d=pts3d,\n confidence=confidence,\n conf_threshold=conf_threshold\n )\n \n print(f\"✓ Extracted {len(colors):,} colors\")\n\n # 画像サイズを取得\n from PIL import Image\n first_img = Image.open(image_paths[0])\n image_size = (first_img.width, first_img.height)\n first_img.close()\n\n # COLMAP出力ディレクトリ\n colmap_dir = os.path.join(output_dir, \"sparse/0\")\n os.makedirs(colmap_dir, exist_ok=True)\n\n # 🔧 FIX: 色付きでエクスポート\n print(\"\\n[Exporting COLMAP files with colors...]\")\n export_colmap_binary_with_colors(\n cameras_dict=cameras_dict,\n pts3d=pts3d,\n confidence=confidence,\n colors=colors, # ← 色を渡す！\n image_size=image_size,\n output_dir=colmap_dir\n )\n\n # メモリクリア\n clear_memory()\n\n # STEP 5: Running Gaussian Splatting\n print(\"\\n\" + \"=\"*70)\n print(\"STEP 5: Running Gaussian Splatting\")\n print(\"=\"*70)\n\n source_dir = output_dir\n model_output_dir = os.path.join(output_dir, \"gaussian_splatting\")\n\n gs_output = run_gaussian_splatting(\n source_dir=source_dir,\n output_dir=model_output_dir,\n iterations=iterations\n )\n\n # STEP 6: Verify Output\n print(\"\\n\" + \"=\"*70)\n print(\"PIPELINE COMPLETE\")\n print(\"=\"*70)\n\n ply_path = os.path.join(\n model_output_dir,\n \"point_cloud\",\n f\"iteration_{iterations}\",\n \"point_cloud.ply\"\n )\n\n if os.path.exists(ply_path):\n file_size = os.path.getsize(ply_path) / (1024 * 1024)\n print(f\"✓ Point cloud generated: {ply_path}\")\n print(f\" Size: {file_size:.2f} MB\")\n else:\n print(f\"⚠️ Point cloud not found at: {ply_path}\")\n\n # 🔧 色の検証\n print(\"\\n[Verifying colors in points3D.bin...]\")\n verify_points3d_colors(os.path.join(colmap_dir, 'points3D.bin'))\n\n print(f\"\\nOutput directory structure:\")\n print(f\" {output_dir}/\")\n print(f\" ├── images/ (processed images)\")\n if preprocess_mode == 'biplet':\n print(f\" ├── original_images/ (original source images)\")\n print(f\" ├── sparse/0/ (COLMAP data WITH COLORS)\")\n print(f\" │ ├── cameras.bin\")\n print(f\" │ ├── images.bin\")\n print(f\" │ └── points3D.bin (✓ WITH ACTUAL COLORS)\")\n print(f\" └── gaussian_splatting/ (GS output)\")\n\n return gs_output\n\n\n# =====================================================================\n# 検証関数: points3D.binの色を確認\n# =====================================================================\n\ndef verify_points3d_colors(points3d_path):\n \"\"\"\n points3D.binの色を確認する\n \"\"\"\n import struct\n \n with open(points3d_path, 'rb') as f:\n num_points = struct.unpack('Q', f.read(8))[0]\n \n colors = []\n for _ in range(min(num_points, 10000)): # 最初の1万点をチェック\n f.read(8) # point_id\n f.read(24) # xyz\n rgb = struct.unpack('BBB', f.read(3))\n colors.append(rgb)\n f.read(8) # error\n track_length = struct.unpack('Q', f.read(8))[0]\n f.read(track_length * 8) # track\n \n colors = np.array(colors)\n unique_colors = len(np.unique(colors, axis=0))\n \n print(f\" Total points: {num_points:,}\")\n print(f\" Sampled: {len(colors):,} points\")\n print(f\" Mean RGB: [{colors.mean(axis=0)[0]:.1f}, {colors.mean(axis=0)[1]:.1f}, {colors.mean(axis=0)[2]:.1f}]\")\n print(f\" Std RGB: [{colors.std(axis=0)[0]:.1f}, {colors.std(axis=0)[1]:.1f}, {colors.std(axis=0)[2]:.1f}]\")\n print(f\" Unique colors: {unique_colors:,}\")\n \n if unique_colors == 1 and colors[0][0] == 128:\n print(\" ❌ All points are GRAY (128, 128, 128)\")\n print(\" ⚠️ Colors were NOT applied!\")\n return False\n else:\n print(\" ✓ Points have ACTUAL colors!\")\n return True\n\n","metadata":{"trusted":true,"id":"U7Lk41hLTKyF","execution":{"iopub.status.busy":"2026-02-01T07:17:30.474200Z","iopub.execute_input":"2026-02-01T07:17:30.474486Z","iopub.status.idle":"2026-02-01T07:17:30.495931Z","shell.execute_reply.started":"2026-02-01T07:17:30.474430Z","shell.execute_reply":"2026-02-01T07:17:30.495114Z"}},"outputs":[],"execution_count":8},{"cell_type":"code","source":"# =====================================================================\n# CELL 15: Run Pipeline\n# =====================================================================\nif __name__ == \"__main__\":\n IMAGE_DIR = \"/kaggle/input/two-dogs/bike15\"\n OUTPUT_DIR = \"/kaggle/working/output\"\n\n\n gs_output = main_pipeline(\n image_dir=IMAGE_DIR,\n output_dir=OUTPUT_DIR,\n square_size=1024,\n iterations=1000,\n max_images=30,\n max_pairs=1000,\n max_points=1000000,\n conf_threshold=1.5, \n preprocess_mode='biplet' # or 'none'\n )\n\n print(\"\\n\" + \"=\"*70)\n print(\"PIPELINE COMPLETE\")\n print(\"=\"*70)\n print(f\"Output directory: {gs_output}\")","metadata":{"trusted":true,"id":"_-8kDLieTKyG","outputId":"beafd1de-a25c-4273-dfcb-10ca5301abb7","execution":{"iopub.status.busy":"2026-02-01T07:17:30.496932Z","iopub.execute_input":"2026-02-01T07:17:30.497858Z","iopub.status.idle":"2026-02-01T07:20:30.898387Z","shell.execute_reply.started":"2026-02-01T07:17:30.497833Z","shell.execute_reply":"2026-02-01T07:20:30.897574Z"}},"outputs":[{"name":"stdout","text":"======================================================================\nSTEP 0: Image Preprocessing (Biplet Crops)\n======================================================================\n\n=== Generating Biplet Crops (1024x1024) ===\n","output_type":"stream"},{"name":"stderr","text":"Creating biplets: 100%|██████████| 15/15 [00:02<00:00, 7.33it/s]\n","output_type":"stream"},{"name":"stdout","text":"\n✓ Biplet generation complete:\n Source images: 15\n Biplet crops generated: 30\n Original size distribution: {'1440x1920': 15}\n✓ Copied 30 biplet images to /kaggle/working/output/images\n✓ Saved 15 original images to /kaggle/working/output/original_images\n\n======================================================================\nSTEP 1: Loading and Preparing Images\n======================================================================\n\nLoading images from: /kaggle/working/output/images\n✓ Found 30 images\nLoaded 30 images\n\n======================================================================\nSTEP 2: Image Pair Selection (DINO)\n======================================================================\n\n=== Extracting DINO Global Features ===\nInitial memory state:\nGPU Memory - Allocated: 0.00GB, Reserved: 0.00GB\nCPU Memory Usage: 5.8%\n","output_type":"stream"},{"name":"stderr","text":"/usr/local/lib/python3.12/dist-packages/huggingface_hub/file_download.py:942: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n warnings.warn(\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"preprocessor_config.json: 0%| | 0.00/436 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"5a2d6a2a21d24bf79d43a75106447ea4"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"config.json: 0%| | 0.00/548 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"01ac7c5469fc4234b62daedc71bf74bc"}},"metadata":{}},{"name":"stderr","text":"/usr/local/lib/python3.12/dist-packages/huggingface_hub/file_download.py:942: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n warnings.warn(\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"model.safetensors: 0%| | 0.00/346M [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"1f639ae63dac4a6db3536e36ddf6801c"}},"metadata":{}},{"name":"stderr","text":"DINO extraction: 100%|██████████| 8/8 [00:10<00:00, 1.34s/it]\n","output_type":"stream"},{"name":"stdout","text":"After DINO extraction:\nGPU Memory - Allocated: 0.03GB, Reserved: 0.06GB\nCPU Memory Usage: 7.9%\nInitial pairs from DINO: 290\nSelecting 50 diverse pairs from 290 candidates...\nSelected pairs cover 30 / 30 images (100.0%)\nSelected 50 image pairs\n\n======================================================================\nSTEP 3: MASt3R 3D Reconstruction\n======================================================================\n\n=== Loading MASt3R Model ===\nAttempting to load: naver/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"config.json: 0%| | 0.00/546 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"6fbe229c6c8d4cf28e87cb463acc1170"}},"metadata":{}},{"name":"stdout","text":"⚠️ Failed to load MASt3R: tried to load naver/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric from huggingface, but failed\nTrying DUSt3R instead: naver/DUSt3R_ViTLarge_BaseDecoder_512_dpt\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"config.json: 0%| | 0.00/450 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"f3a0dd313bc240048d47e1e21ed42074"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"model.safetensors: 0%| | 0.00/2.28G [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"deb773553b9749f0b341e04e2b968f3d"}},"metadata":{}},{"name":"stdout","text":"✓ Loaded DUSt3R model as fallback\n✓ Model loaded on cuda\n\n=== Running MASt3R Reconstruction ===\nInitial memory state:\nGPU Memory - Allocated: 2.14GB, Reserved: 2.14GB\nCPU Memory Usage: 15.3%\n","output_type":"stream"},{"name":"stderr","text":"/kaggle/working/mast3r/dust3r/dust3r/cloud_opt/base_opt.py:275: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n @torch.cuda.amp.autocast(enabled=False)\n","output_type":"stream"},{"name":"stdout","text":"Processing 50 pairs...\nLoading 30 images at 224x224...\n>> Loading a list of 30 images\n - adding /kaggle/working/output/images/image_004_bottom.jpeg with resolution 1024x1024 --> 224x224\n - adding /kaggle/working/output/images/image_004_top.jpeg with resolution 1024x1024 --> 224x224\n - adding /kaggle/working/output/images/image_029_bottom.jpeg with resolution 1024x1024 --> 224x224\n - adding /kaggle/working/output/images/image_029_top.jpeg with resolution 1024x1024 --> 224x224\n - adding /kaggle/working/output/images/image_038_bottom.jpeg with resolution 1024x1024 --> 224x224\n - adding /kaggle/working/output/images/image_038_top.jpeg with resolution 1024x1024 --> 224x224\n - adding /kaggle/working/output/images/image_049_bottom.jpeg with resolution 1024x1024 --> 224x224\n - adding /kaggle/working/output/images/image_049_top.jpeg with resolution 1024x1024 --> 224x224\n - adding /kaggle/working/output/images/image_062_bottom.jpeg with resolution 1024x1024 --> 224x224\n - adding /kaggle/working/output/images/image_062_top.jpeg with resolution 1024x1024 --> 224x224\n - adding /kaggle/working/output/images/image_076_bottom.jpeg with resolution 1024x1024 --> 224x224\n - adding /kaggle/working/output/images/image_076_top.jpeg with resolution 1024x1024 --> 224x224\n - adding /kaggle/working/output/images/image_088_bottom.jpeg with resolution 1024x1024 --> 224x224\n - adding /kaggle/working/output/images/image_088_top.jpeg with resolution 1024x1024 --> 224x224\n - adding /kaggle/working/output/images/image_094_bottom.jpeg with resolution 1024x1024 --> 224x224\n - adding /kaggle/working/output/images/image_094_top.jpeg with resolution 1024x1024 --> 224x224\n - adding /kaggle/working/output/images/image_101_bottom.jpeg with resolution 1024x1024 --> 224x224\n - adding /kaggle/working/output/images/image_101_top.jpeg with resolution 1024x1024 --> 224x224\n - adding /kaggle/working/output/images/image_115_bottom.jpeg with resolution 1024x1024 --> 224x224\n - adding /kaggle/working/output/images/image_115_top.jpeg with resolution 1024x1024 --> 224x224\n - adding /kaggle/working/output/images/image_119_bottom.jpeg with resolution 1024x1024 --> 224x224\n - adding /kaggle/working/output/images/image_119_top.jpeg with resolution 1024x1024 --> 224x224\n - adding /kaggle/working/output/images/image_128_bottom.jpeg with resolution 1024x1024 --> 224x224\n - adding /kaggle/working/output/images/image_128_top.jpeg with resolution 1024x1024 --> 224x224\n - adding /kaggle/working/output/images/image_137_bottom.jpeg with resolution 1024x1024 --> 224x224\n - adding /kaggle/working/output/images/image_137_top.jpeg with resolution 1024x1024 --> 224x224\n - adding /kaggle/working/output/images/image_139_bottom.jpeg with resolution 1024x1024 --> 224x224\n - adding /kaggle/working/output/images/image_139_top.jpeg with resolution 1024x1024 --> 224x224\n - adding /kaggle/working/output/images/image_150_bottom.jpeg with resolution 1024x1024 --> 224x224\n - adding /kaggle/working/output/images/image_150_top.jpeg with resolution 1024x1024 --> 224x224\n (Found 30 images)\nLoaded 30 images\nAfter loading images:\nGPU Memory - Allocated: 2.14GB, Reserved: 2.14GB\nCPU Memory Usage: 15.4%\nCreating 50 image pairs...\n","output_type":"stream"},{"name":"stderr","text":"Preparing pairs: 100%|██████████| 50/50 [00:00<00:00, 322638.77it/s]\n","output_type":"stream"},{"name":"stdout","text":"Running MASt3R inference on 50 pairs...\n>> Inference with model on 50 image pairs\n","output_type":"stream"},{"name":"stderr","text":" 0%| | 0/50 [00:00<?, ?it/s]/kaggle/working/mast3r/dust3r/dust3r/inference.py:44: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n with torch.cuda.amp.autocast(enabled=bool(use_amp)):\n/kaggle/working/mast3r/dust3r/dust3r/model.py:206: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n with torch.cuda.amp.autocast(enabled=False):\n/kaggle/working/mast3r/dust3r/dust3r/inference.py:48: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n with torch.cuda.amp.autocast(enabled=False):\n100%|██████████| 50/50 [00:09<00:00, 5.24it/s]\n","output_type":"stream"},{"name":"stdout","text":"✓ MASt3R inference complete\nAfter inference:\nGPU Memory - Allocated: 2.14GB, Reserved: 2.14GB\nCPU Memory Usage: 15.7%\nRunning global alignment...\nComputing global alignment...\n init edge (4*,22*) score=61.01396942138672\n init edge (8*,22) score=50.93975830078125\n init edge (8,24*) score=24.73683738708496\n init edge (6*,22) score=23.54641342163086\n init edge (8,20*) score=23.08745002746582\n init edge (18*,24) score=22.09931755065918\n init edge (0*,22) score=18.92777442932129\n init edge (24,25*) score=16.81424331665039\n init edge (5*,22) score=16.769275665283203\n init edge (12*,22) score=16.5622501373291\n init edge (0,14*) score=13.714292526245117\n init edge (19*,25) score=11.434065818786621\n init edge (22,23*) score=11.384899139404297\n init edge (1*,22) score=10.828630447387695\n init edge (11*,25) score=8.875144004821777\n init edge (4,7*) score=8.490281105041504\n init edge (2*,22) score=8.216402053833008\n init edge (12,29*) score=6.481838703155518\n init edge (15*,22) score=6.353991508483887\n init edge (10*,24) score=6.015658855438232\n init edge (17*,24) score=5.83776330947876\n init edge (13*,19) score=3.9419987201690674\n init edge (21*,25) score=23.639514923095703\n init edge (20,26*) score=23.604904174804688\n init edge (12,16*) score=16.978376388549805\n init edge (3*,16) score=9.270556449890137\n init edge (9*,26) score=7.399363040924072\n init edge (26,28*) score=7.344329357147217\n init edge (26,27*) score=27.2646541595459\n init loss = 0.027633165940642357\nGlobal alignement - optimizing for:\n['pw_poses', 'im_depthmaps', 'im_poses', 'im_focals']\n","output_type":"stream"},{"name":"stderr","text":" 0%| | 0/50 [00:00<?, ?it/s]/kaggle/working/mast3r/dust3r/dust3r/cloud_opt/base_opt.py:366: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n return float(loss), lr\n100%|██████████| 50/50 [00:02<00:00, 19.75it/s, lr=1.08654e-05 loss=0.0141021]\n","output_type":"stream"},{"name":"stdout","text":"✓ Global alignment complete (final loss: 0.014102)\nFinal memory state:\nGPU Memory - Allocated: 2.32GB, Reserved: 2.70GB\nCPU Memory Usage: 16.1%\n\n======================================================================\nSTEP 4: Converting to COLMAP (PINHOLE) WITH COLORS\n======================================================================\n\n=== Extracting Camera Parameters ===\n\nExample camera 0:\n Original size: 1024x1024\n MASt3R size: 224.0\n Scale factor: 4.571\n focals.shape: torch.Size([30, 1])\n MASt3R focal: 289.39\n Scaled focal: fx = fy = 1322.93\n MASt3R pp: [112.00, 112.00]\n Scaled pp: [512.00, 512.00]\n✓ Extracted parameters for 30 cameras\n✓ Total 3D points: 1505280\n✓ Points after confidence filtering (>1.5): 1179674\nExtracted 30 cameras with conf >= 1.5\nExtracted 1,179,674 3D points\n\n[Extracting colors from images...]\n\n=== Extracting Colors from Images ===\nExtracting colors from 30 images...\n Example image 0:\n Original size: 1024x1024\n Resized to: 224x224\n Colors shape: (50176, 3)\n✓ Total colors extracted: 1,505,280\n✓ Colors after confidence filtering (>1.5): 1,179,674\n✓ Colors match points: 1,179,674 colors for 1,179,674 points\n✓ Unique colors: 110,634\n✓ Good color diversity\n✓ Extracted 1,179,674 colors\n\n[Exporting COLMAP files with colors...]\nCOLMAP cameras.bin saved to /kaggle/working/output/sparse/0/cameras.bin\nCOLMAP images.bin saved to /kaggle/working/output/sparse/0/images.bin\nCOLMAP points3D.bin saved to /kaggle/working/output/sparse/0/points3D.bin\n ✓ With actual RGB colors from images!\n\n✓ COLMAP binary files exported to /kaggle/working/output/sparse/0/\n - cameras.bin: 30 cameras (PINHOLE model)\n - images.bin: 30 images\n - points3D.bin: 1179674 points WITH COLORS\n\n======================================================================\nSTEP 5: Running Gaussian Splatting\n======================================================================\n\n=== Running Gaussian Splatting ===\nCommand: python /kaggle/working/gaussian-splatting/train.py -s /kaggle/working/output -m /kaggle/working/output/gaussian_splatting --iterations 1000 --eval\n Source: /kaggle/working/output\n Output: /kaggle/working/output/gaussian_splatting\n","output_type":"stream"},{"name":"stderr","text":"2026-02-01 07:18:49.282970: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\nWARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nE0000 00:00:1769930329.304553 766 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\nE0000 00:00:1769930329.311187 766 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\nW0000 00:00:1769930329.328184 766 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1769930329.328217 766 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1769930329.328220 766 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1769930329.328223 766 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n","output_type":"stream"},{"name":"stdout","text":"Optimizing /kaggle/working/output/gaussian_splatting\nOutput folder: /kaggle/working/output/gaussian_splatting [01/02 07:18:53]\n------------LLFF HOLD------------- [01/02 07:18:53]\nReading camera 30/30 [01/02 07:18:53]\nConverting point3d.bin to .ply, will happen only the first time you open the scene. [01/02 07:18:53]\nLoading Training Cameras [01/02 07:19:00]\nLoading Test Cameras [01/02 07:19:02]\nNumber of points at initialisation : 1179674 [01/02 07:19:02]\n","output_type":"stream"},{"name":"stderr","text":"Training progress: 100%|██████████| 1000/1000 [01:12<00:00, 13.79it/s, Loss=0.2473092, Depth Loss=0.0000000]\n","output_type":"stream"},{"name":"stdout","text":"\n[ITER 1000] Saving Gaussians [01/02 07:20:17]\n\nTraining complete. [01/02 07:20:29]\n\n✓ Gaussian Splatting complete\n\n✓ Point cloud directory found: /kaggle/working/output/gaussian_splatting/point_cloud\n ✓ iteration_1000/point_cloud.ply (296.52 MB)\n\n======================================================================\nPIPELINE COMPLETE\n======================================================================\n✓ Point cloud generated: /kaggle/working/output/gaussian_splatting/point_cloud/iteration_1000/point_cloud.ply\n Size: 296.52 MB\n\n[Verifying colors in points3D.bin...]\n Total points: 1,179,674\n Sampled: 10,000 points\n Mean RGB: [124.7, 120.5, 115.1]\n Std RGB: [64.8, 60.7, 57.4]\n Unique colors: 7,437\n ✓ Points have ACTUAL colors!\n\nOutput directory structure:\n /kaggle/working/output/\n ├── images/ (processed images)\n ├── original_images/ (original source images)\n ├── sparse/0/ (COLMAP data WITH COLORS)\n │ ├── cameras.bin\n │ ├── images.bin\n │ └── points3D.bin (✓ WITH ACTUAL COLORS)\n └── gaussian_splatting/ (GS output)\n\n======================================================================\nPIPELINE COMPLETE\n======================================================================\nOutput directory: /kaggle/working/output/gaussian_splatting\n","output_type":"stream"}],"execution_count":9},{"cell_type":"code","source":"def convert_colmap_bins_to_csv(sparse_dir, output_prefix):\n \"\"\"Convert all COLMAP binary files in a directory to CSV format.\"\"\"\n print(f\"\\n=== Converting {sparse_dir} to CSV ===\")\n\n cameras_df = bin_to_csv_cameras(\n os.path.join(sparse_dir, 'cameras.bin'),\n f\"{output_prefix}_cameras.csv\"\n )\n\n images_df = bin_to_csv_images(\n os.path.join(sparse_dir, 'images.bin'),\n f\"{output_prefix}_images.csv\"\n )\n\n points_df = bin_to_csv_points3d(\n os.path.join(sparse_dir, 'points3D.bin'),\n f\"{output_prefix}_points3d.csv\",\n max_rows=10000\n )\n\n return cameras_df, images_df, points_df","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-02-01T07:20:30.899479Z","iopub.execute_input":"2026-02-01T07:20:30.900402Z","iopub.status.idle":"2026-02-01T07:20:30.905729Z","shell.execute_reply.started":"2026-02-01T07:20:30.900373Z","shell.execute_reply":"2026-02-01T07:20:30.904951Z"}},"outputs":[],"execution_count":10},{"cell_type":"code","source":"convert_colmap_bins_to_csv('/kaggle/working/output/sparse/0', 'output')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-02-01T07:20:30.906717Z","iopub.execute_input":"2026-02-01T07:20:30.907383Z","iopub.status.idle":"2026-02-01T07:20:32.142689Z","shell.execute_reply.started":"2026-02-01T07:20:30.907356Z","shell.execute_reply":"2026-02-01T07:20:32.141939Z"}},"outputs":[{"name":"stdout","text":"\n=== Converting /kaggle/working/output/sparse/0 to CSV ===\n✓ Cameras CSV saved: output_cameras.csv\n✓ Images CSV saved: output_images.csv\n✓ Points3D CSV saved: output_points3d.csv (sampled 10083 / 1179674 points)\n","output_type":"stream"},{"execution_count":11,"output_type":"execute_result","data":{"text/plain":"( camera_id model_id width height fx fy cx cy\n 0 1 1 1024 1024 1322.931920 1322.931920 512.0 512.0\n 1 2 1 1024 1024 1484.335938 1484.335938 512.0 512.0\n 2 3 1 1024 1024 1107.123884 1107.123884 512.0 512.0\n 3 4 1 1024 1024 971.500488 971.500488 512.0 512.0\n 4 5 1 1024 1024 988.139369 988.139369 512.0 512.0\n 5 6 1 1024 1024 2137.385603 2137.385603 512.0 512.0\n 6 7 1 1024 1024 1162.021554 1162.021554 512.0 512.0\n 7 8 1 1024 1024 1359.039621 1359.039621 512.0 512.0\n 8 9 1 1024 1024 980.173898 980.173898 512.0 512.0\n 9 10 1 1024 1024 1605.229911 1605.229911 512.0 512.0\n 10 11 1 1024 1024 1376.408343 1376.408343 512.0 512.0\n 11 12 1 1024 1024 972.693569 972.693569 512.0 512.0\n 12 13 1 1024 1024 1045.688825 1045.688825 512.0 512.0\n 13 14 1 1024 1024 1337.419224 1337.419224 512.0 512.0\n 14 15 1 1024 1024 1313.158901 1313.158901 512.0 512.0\n 15 16 1 1024 1024 1146.235561 1146.235561 512.0 512.0\n 16 17 1 1024 1024 1058.092704 1058.092704 512.0 512.0\n 17 18 1 1024 1024 1334.102818 1334.102818 512.0 512.0\n 18 19 1 1024 1024 1132.095215 1132.095215 512.0 512.0\n 19 20 1 1024 1024 1064.045759 1064.045759 512.0 512.0\n 20 21 1 1024 1024 1181.065569 1181.065569 512.0 512.0\n 21 22 1 1024 1024 1183.370675 1183.370675 512.0 512.0\n 22 23 1 1024 1024 1373.632254 1373.632254 512.0 512.0\n 23 24 1 1024 1024 1494.968750 1494.968750 512.0 512.0\n 24 25 1 1024 1024 1050.758998 1050.758998 512.0 512.0\n 25 26 1 1024 1024 1034.205148 1034.205148 512.0 512.0\n 26 27 1 1024 1024 1032.725237 1032.725237 512.0 512.0\n 27 28 1 1024 1024 1986.463728 1986.463728 512.0 512.0\n 28 29 1 1024 1024 1145.189872 1145.189872 512.0 512.0\n 29 30 1 1024 1024 1058.677037 1058.677037 512.0 512.0,\n image_id qw qx qy qz tx ty \\\n 0 1 0.927881 -0.072171 -0.275087 -0.241155 0.070648 -0.055844 \n 1 2 0.901012 -0.213078 -0.306379 -0.221150 0.065354 -0.048409 \n 2 3 0.975904 -0.183692 -0.070888 -0.094046 0.017266 -0.065307 \n 3 4 0.931620 -0.338329 -0.101396 -0.085652 0.020074 -0.037644 \n 4 5 0.999971 0.007519 0.001142 -0.000494 0.000000 0.000000 \n 5 6 0.976464 -0.213400 0.002227 -0.031195 -0.004513 -0.032888 \n 6 7 0.937563 -0.091922 0.305402 0.138763 -0.034499 -0.040977 \n 7 8 0.925227 -0.220974 0.285658 0.116298 -0.024236 -0.027639 \n 8 9 0.455139 0.042332 0.686274 0.565759 -0.066975 -0.071841 \n 9 10 0.461329 -0.053338 0.784942 0.410117 -0.063074 -0.046496 \n 10 11 -0.323792 0.054836 0.751358 0.572375 0.062037 -0.116881 \n 11 12 -0.341530 0.061126 0.769578 0.536070 0.061409 -0.062090 \n 12 13 0.743379 -0.100693 -0.534237 -0.389665 0.066534 -0.077246 \n 13 14 0.719731 -0.192419 -0.582786 -0.324535 0.058070 -0.041754 \n 14 15 0.795386 -0.146187 -0.499929 -0.309939 0.070590 -0.101209 \n 15 16 0.772093 -0.239315 -0.535441 -0.244753 0.069395 -0.056762 \n 16 17 0.641013 0.038547 -0.515508 -0.567334 0.069671 -0.069802 \n 17 18 0.654595 -0.048367 -0.591290 -0.468553 0.068984 -0.040825 \n 18 19 -0.253873 -0.029941 0.854300 0.452575 0.038126 -0.110592 \n 19 20 -0.247055 0.030679 0.892992 0.374950 0.031066 -0.049462 \n 20 21 0.115099 0.009068 0.835608 0.537056 -0.053340 -0.116988 \n 21 22 0.148318 -0.023712 0.879064 0.452422 -0.054064 -0.055615 \n 22 23 0.746056 0.001660 0.564887 0.352562 -0.107388 -0.066273 \n 23 24 0.728594 -0.097467 0.625235 0.262170 -0.109616 -0.029967 \n 24 25 0.187544 -0.019760 0.695446 0.693391 -0.032679 -0.039008 \n 25 26 0.244687 -0.132188 0.848541 0.450148 -0.013684 -0.053263 \n 26 27 -0.058081 0.018830 0.686333 0.724720 0.007058 -0.129410 \n 27 28 -0.048744 0.010848 0.794787 0.604831 0.011557 -0.078974 \n 28 29 -0.397708 0.106902 0.751044 0.516074 0.050834 -0.109185 \n 29 30 -0.426805 0.171233 0.790533 0.404443 0.055331 -0.055592 \n \n tz camera_id name \n 0 0.062959 1 image_004_bottom.jpeg \n 1 0.073141 2 image_004_top.jpeg \n 2 0.103272 3 image_029_bottom.jpeg \n 3 0.087217 4 image_029_top.jpeg \n 4 0.000000 5 image_038_bottom.jpeg \n 5 0.043278 6 image_038_top.jpeg \n 6 0.017817 7 image_049_bottom.jpeg \n 7 0.077299 8 image_049_top.jpeg \n 8 0.113223 9 image_062_bottom.jpeg \n 9 0.168308 10 image_062_top.jpeg \n 10 0.168626 11 image_076_bottom.jpeg \n 11 0.143139 12 image_076_top.jpeg \n 12 0.089899 13 image_088_bottom.jpeg \n 13 0.145371 14 image_088_top.jpeg \n 14 0.153376 15 image_094_bottom.jpeg \n 15 0.161768 16 image_094_top.jpeg \n 16 0.078398 17 image_101_bottom.jpeg \n 17 0.120976 18 image_101_top.jpeg \n 18 0.193647 19 image_115_bottom.jpeg \n 19 0.216639 20 image_115_top.jpeg \n 20 0.201523 21 image_119_bottom.jpeg \n 21 0.212228 22 image_119_top.jpeg \n 22 0.147423 23 image_128_bottom.jpeg \n 23 0.175961 24 image_128_top.jpeg \n 24 0.197837 25 image_137_bottom.jpeg \n 25 0.070437 26 image_137_top.jpeg \n 26 0.132166 27 image_139_bottom.jpeg \n 27 0.301035 28 image_139_top.jpeg \n 28 0.158028 29 image_150_bottom.jpeg \n 29 0.167978 30 image_150_top.jpeg ,\n point_id x y z r g b error\n 0 1 -0.029637 -0.037321 0.098506 43 47 52 0.648973\n 1 118 -0.028862 -0.034588 0.097750 35 40 44 0.380816\n 2 235 -0.032865 -0.036781 0.101978 56 59 62 0.451116\n 3 352 0.120412 -0.070081 0.213010 60 56 50 0.593942\n 4 469 0.119345 -0.069761 0.215893 106 102 90 0.587462\n ... ... ... ... ... ... ... ... ...\n 10078 1179127 -0.022655 0.006154 0.131004 56 56 54 0.400867\n 10079 1179244 0.035582 -0.011458 0.167491 172 162 153 0.252837\n 10080 1179361 -0.023425 0.012970 0.128365 116 121 126 0.384711\n 10081 1179478 0.029623 -0.007577 0.163603 79 70 65 0.268693\n 10082 1179595 -0.022451 0.025236 0.124421 189 180 171 0.167128\n \n [10083 rows x 8 columns])"},"metadata":{}}],"execution_count":11},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null}]} |